{"title":"Computer vision Books","description":"","products":[{"product_id":"introduction-to-biometrics-9780387773254","title":"Introduction to Biometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48733726310743,"sku":"9780387773254","price":59.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780387773254.jpg?v=1720001398"},{"product_id":"explainable-ai-for-practitioners-9781098119133","title":"Explainable AI for Practitioners","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eExplainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48738226798935,"sku":"9781098119133","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098119133.jpg?v=1723811837"},{"product_id":"unsupervised-learning-in-space-and-time-a-modern-approach-for-computer-vision-using-graph-based-techniques-and-deep-neural-networks-9783030421274","title":"Unsupervised Learning in Space and Time: A Modern","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field.\u003c\/p\u003e  \u003cp\u003ePresenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.\u003c\/p\u003e  \u003cp\u003eOffering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.\u003c\/p\u003e  \u003cp\u003eServing as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines. \u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e1. Unsupervised Visual Learning: from Pixels to Seeing.- 2. Unsupervised Learning of Graph and Hypergraph Matching.- 3. Unsupervised Learning of Graph and Hypergraph Clustering.- 4. Feature Selection meets Unsupervised Learning.- 5. Unsupervised Learning of Object Segmentation in Video with Highly Probable Positive Features.- 6. Coupling Appearance and Motion: Unsupervised Clustering for Object Segmentation through Space and Time.- 7. Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks.- 8. Unsupervised Learning Towards the Future.\u003cbr\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743035634007,"sku":"9783030421274","price":113.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030421274.jpg?v=1720063826"},{"product_id":"handbook-of-digital-face-manipulation-and-detection-from-deepfakes-to-morphing-attacks-9783030876661","title":"Handbook of Digital Face Manipulation and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePart I - Introduction:\u003c\/b\u003e 1. Digital Face Manipulation: An Introduction.- 2. Face Manipulation in Biometric Systems.- 3. Face Manipulation in Media Forensics.- \u003cb\u003ePart II - Face Manipulation Detection Methods: \u003c\/b\u003e4. DeepFakes Manipulation.- 5. DeepFakes Detection.- 6. Attacking Face Recognition Systems with DeepFakes.- 7. Vulnerability of Face Recognition Systems to Morphing Attacks.- 8. Face Morphing Attack Detection.- 9. Face Synthesis Detection.- 10. Expression Swap Detection.- 11. Audio- and Text-to-Video Detection.- 12. Detection of Facial Retouching.- 13. Face De-Identification Detection.- \u003cb\u003ePart III - Further Topics:\u003c\/b\u003e 14. All-in-One Face Manipulation Detection: Generalization Analysis.- 15. Reversion of Face Manipulation.- 16. 3D Face Manipulation Detection.- 17. Improving Face Recognition with Face Image Manipulation.- 18. Impact of Post-Processing on Face Manipulation Detection.- 19. Societal and Legal Aspects of Face Manipulation.- 20. Face Manipulation for Privacy Protection.- 21. Privacy-preserving Face Manipulation Detection.- 22. Face Manipulation in Operational Systems.- \u003cb\u003ePart IV - Open Issues, Trends, and Challenges: \u003c\/b\u003e23. All: Future trends in face Manipulation and Fake Detection.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743055917399,"sku":"9783030876661","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030876661.jpg?v=1720063916"},{"product_id":"misinformation-and-disinformation-detecting-fakes-with-the-eye-and-ai-9783030956554","title":"Misinformation and Disinformation: Detecting","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book, geared towards both students and professionals, examines the synthesis of artificial intelligence (AI) and psychology in detecting mis-\/disinformation in digital media content, and suggests practical means to intervene and curtail this current global ‘infodemic’. This interdisciplinary book explores technological, psychological, philosophical, and linguistic insights into the nature of truth and deception, trust and credibility, cognitive biases and logical fallacies and how, through AI and human intervention, content users can be alerted to the presence of deception. The author investigates how AI can mimic the procedures and know-hows of humans, showing how AI can help spot fakes and how AI tools can work to debunk rumors and fact-check. The book describes how AI detection systems work and how they fit with broader societal and individual concerns. Each chapter focuses attention on key concepts and their inter-connection. The first part of the book seeks theoretical footing to understand our interactions with new information and reviews relevant empirical findings in behavioral sciences. The second part is about applied knowledge. The author looks at several known practices that guard us against deception, and provides several real-world examples of manipulative persuasive techniques in advertising, political propaganda, and public relations. She provides links to the downloadable executable files to three AI applications (clickbait, satire, and falsehood detectors) via LiT.RL GitHub, an open access repository. The book is useful to students and professionals studying AI and media studies as well as library and information professionals.\u003cul\u003e\n\u003cli\u003eExamines how artificial intelligence (AI) and psychology can aid in detecting mis-\/disinformation and the language of deceit in digital media content;\u003c\/li\u003e\n\u003cli\u003eSuggests practical computational means to intervene and curtail the global ‘infodemic’ of fake news;\u003c\/li\u003e\n\u003cli\u003ePresents how AI can sift, sort, and shuffle digital content, to reduce the amount of content needed to be reviewed by humans.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction.- Infodemic in the Digital Media Content.- Part I. Human Nature of Deception and Perceptions of Truth.- Psychology of Mis-\/Disinformation and Language of Deceit.- Library and Information Science on Credibility and Trust in Computing.- Philosophies of Truth.- Part II. Applied Practices and Artificial Intelligence (AI).- Investigation in Law Enforcement, Journalism, and Sciences.- Manipulation in Marketing, Advertising, and Public Relations.- Artificially Intelligent Solutions: Detection, Debunking, Fact-Checking.- Lessons for Infodemic Control and Future of Digital Content Verification.- Conclusion.\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743061094743,"sku":"9783030956554","price":52.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030956554.jpg?v=1720063940"},{"product_id":"misinformation-and-disinformation-detecting-fakes-with-the-eye-and-ai-9783030956585","title":"Misinformation and Disinformation: Detecting","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book, geared towards both students and professionals, examines the synthesis of artificial intelligence (AI) and psychology in detecting mis-\/disinformation in digital media content, and suggests practical means to intervene and curtail this current global ‘infodemic’. This interdisciplinary book explores technological, psychological, philosophical, and linguistic insights into the nature of truth and deception, trust and credibility, cognitive biases and logical fallacies and how, through AI and human intervention, content users can be alerted to the presence of deception. The author investigates how AI can mimic the procedures and know-hows of humans, showing how AI can help spot fakes and how AI tools can work to debunk rumors and fact-check. The book describes how AI detection systems work and how they fit with broader societal and individual concerns. Each chapter focuses attention on key concepts and their inter-connection. The first part of the book seeks theoretical footing to understand our interactions with new information and reviews relevant empirical findings in behavioral sciences. The second part is about applied knowledge. The author looks at several known practices that guard us against deception, and provides several real-world examples of manipulative persuasive techniques in advertising, political propaganda, and public relations. She provides links to the downloadable executable files to three AI applications (clickbait, satire, and falsehood detectors) via LiT.RL GitHub, an open access repository. The book is useful to students and professionals studying AI and media studies as well as library and information professionals.\u003cul\u003e\n\u003cli\u003eExamines how artificial intelligence (AI) and psychology can aid in detecting mis-\/disinformation and the language of deceit in digital media content;\u003c\/li\u003e\n\u003cli\u003eSuggests practical computational means to intervene and curtail the global ‘infodemic’ of fake news;\u003c\/li\u003e\n\u003cli\u003ePresents how AI can sift, sort, and shuffle digital content, to reduce the amount of content needed to be reviewed by humans.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction.- Infodemic in the Digital Media Content.- Part I. Human Nature of Deception and Perceptions of Truth.- Psychology of Mis-\/Disinformation and Language of Deceit.- Library and Information Science on Credibility and Trust in Computing.- Philosophies of Truth.- Part II. Applied Practices and Artificial Intelligence (AI).- Investigation in Law Enforcement, Journalism, and Sciences.- Manipulation in Marketing, Advertising, and Public Relations.- Artificially Intelligent Solutions: Detection, Debunking, Fact-Checking.- Lessons for Infodemic Control and Future of Digital Content Verification.- Conclusion.\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743061258583,"sku":"9783030956585","price":40.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030956585.jpg?v=1720063940"},{"product_id":"computer-vision-statistical-models-for-marrs-paradigm-9783030965297","title":"Computer Vision: Statistical Models for Marr's","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAs the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Béla Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. The authors hope to provide foundation and, perhaps more importantly, further inspiration for continued research in vision. This book covers David Marr's paradigm and various underlying statistical models for vision. The mathematical framework herein integrates three regimes of models (low-, mid-, and high-entropy regimes) and provides foundation for research in visual coding, recognition, and cognition. Concepts are first explained for understanding and then supported by findings in psychology and neuroscience, after which they are established by statistical models and associated learning and inference algorithms. 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As software systems are becoming larger and complex, software engineering tasks become increasingly costly and prone to errors. Evolutionary algorithms, machine learning approaches, meta-heuristic algorithms, and others techniques can help the effi ciency of software engineering. \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48743090159959,"sku":"9783110705430","price":101.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110705430.jpg?v=1720064066"},{"product_id":"linear-algebra-and-optimization-with-applications-to-machine-learning-volume-i-linear-algebra-for-computer-vision-robotics-and-machine-learning-9789811207716","title":"Linear Algebra And Optimization With Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743276052823,"sku":"9789811207716","price":81.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811207716.jpg?v=1720064881"},{"product_id":"computational-geometry-with-independent-and-dependent-uncertainties-9789811253836","title":"Computational Geometry With Independent And","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis comprehensive compendium describes a parametric model and algorithmic theory  to represent geometric entities with dependent uncertainties between them. The theory, named Linear Parametric Geometric Uncertainty Model (LPGUM), is an expressive and computationally efficient framework that allows to systematically study geometric  uncertainty and its related algorithms in computer geometry.The self-contained monograph is of great scientific, technical, and economic importance  as geometric uncertainty is ubiquitous in mechanical CAD\/CAM, robotics, computer vision, wireless networks and many other fields. Geometric models, in contrast, are usually exact and do not account for these inaccuracies.This useful reference text benefits academics, researchers, and practitioners in computer  science, robotics, mechanical engineering and related fields.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743284998487,"sku":"9789811253836","price":63.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811253836.jpg?v=1720064924"},{"product_id":"pixels-paintings-9780470229446","title":"Pixels  Paintings","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book is a collection representing some of the most powerful and useful computer techniques in the service of art.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Figures xxi\u003c\/p\u003e \u003cp\u003eList of Tables xlv\u003c\/p\u003e \u003cp\u003eList of Algorithms xlvii\u003c\/p\u003e \u003cp\u003ePreface xlix\u003c\/p\u003e \u003cp\u003eLorenzo Lotto lviii\u003c\/p\u003e \u003cp\u003eGiovanni Morelli and the birth of \"scientific\" connoisseurship lix\u003c\/p\u003e \u003cp\u003eOverview lxi\u003c\/p\u003e \u003cp\u003eIntended audience lxii\u003c\/p\u003e \u003cp\u003ePrerequisites lxiii\u003c\/p\u003e \u003cp\u003eAcknowledgements lxiv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Digital imaging 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Electromagnetic radiation and light 4\u003c\/p\u003e \u003cp\u003e1.3 Interaction of electromagnetic radiation with art materials 7\u003c\/p\u003e \u003cp\u003e1.4 Cameras and scanners 9\u003c\/p\u003e \u003cp\u003e1.4.1 Cameras 10\u003c\/p\u003e \u003cp\u003e1.4.2 Flatbed scanners 11\u003c\/p\u003e \u003cp\u003e1.5 Parameters for image acquisition in the visible 12\u003c\/p\u003e \u003cp\u003eBilly Pappas 13\u003c\/p\u003e \u003cp\u003e1.5.1 Spatial resolution 15\u003c\/p\u003e \u003cp\u003e1.5.2 Bit depth 16\u003c\/p\u003e \u003cp\u003e1.5.3 Dynamic range and contrast 17\u003c\/p\u003e \u003cp\u003e1.6 Reading digital images of art on–screen 18\u003c\/p\u003e \u003cp\u003e1.6.1 Reading a digital image of Leonardo's La Bella Principessa 22\u003c\/p\u003e \u003cp\u003eLeonardo da Vinci 22\u003c\/p\u003e \u003cp\u003e1.7 Infrared photography and reflectography 25\u003c\/p\u003e \u003cp\u003e1.8 Ultraviolet imaging 26\u003c\/p\u003e \u003cp\u003e1.9 Multispectral and hyperspectral imaging 27\u003c\/p\u003e \u003cp\u003e1.9.1 Hyperspectral imaging of the Archimedes Palimpsest 30\u003c\/p\u003e \u003cp\u003e1.10 X-radiographic imaging 32\u003c\/p\u003e \u003cp\u003e1.11 Fluorescence imaging 35\u003c\/p\u003e \u003cp\u003e1.12 Capture of three–dimensional surfaces of art 37\u003c\/p\u003e \u003cp\u003e1.12.1 Raking illumination 38\u003c\/p\u003e \u003cp\u003e1.12.2 Reflectance transformation imaging (RTI) 40\u003c\/p\u003e \u003cp\u003e1.12.3 Stereographic imaging 42\u003c\/p\u003e \u003cp\u003e1.13 Optical coherence tomography (OCT) 43\u003c\/p\u003e \u003cp\u003e1.14 Raman spectroscopic imaging and X-ray fluorescence imaging 45\u003c\/p\u003e \u003cp\u003e1.14.1 Raman spectroscopic imaging (RSI) 45\u003c\/p\u003e \u003cp\u003e1.14.2 X-ray fluorescence imaging (XRF) 46\u003c\/p\u003e \u003cp\u003e1.15 Summary 47\u003c\/p\u003e \u003cp\u003e1.16 Bibliographical remarks 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Image processing 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 53\u003c\/p\u003e \u003cp\u003e2.2 Pixel–based image processing 57\u003c\/p\u003e \u003cp\u003e2.3 Region–based image processing 61\u003c\/p\u003e \u003cp\u003e2.3.1 Linear image processing 62\u003c\/p\u003e \u003cp\u003e2.3.2 Nonlinear region–based image processing 63\u003c\/p\u003e \u003cp\u003e2.3.3 Color quantization 64\u003c\/p\u003e \u003cp\u003e2.3.4 Edge and line detection 69\u003c\/p\u003e \u003cp\u003e2.3.5 Dilation and erosion 71\u003c\/p\u003e \u003cp\u003e2.3.6 Skeletonization 72\u003c\/p\u003e \u003cp\u003e2.4 Inpainting 72\u003c\/p\u003e \u003cp\u003e2.5 Feature extraction 74\u003c\/p\u003e \u003cp\u003e2.5.1 Keypoint extraction 75\u003c\/p\u003e \u003cp\u003e2.5.2 Craquelure and crazing analysis 78\u003c\/p\u003e \u003cp\u003e2.5.3 Computational tests for counterproofing by Jan van der Heyden 81\u003c\/p\u003e \u003cp\u003eJan van der Heyden 83\u003c\/p\u003e \u003cp\u003e2.6 Segmentation 86\u003c\/p\u003e \u003cp\u003e2.6.1 Deep nets for image segmentation 88\u003c\/p\u003e \u003cp\u003e2.7 Geometric transformations 95\u003c\/p\u003e \u003cp\u003e2.8 Chamfer transform and Chamfer distance 101\u003c\/p\u003e \u003cp\u003e2.8.1 Tests for copying of Jan van Eyck's portraits of Niccolò Albergati 103\u003c\/p\u003e \u003cp\u003e2.9 Discrete Fourier and wavelet transforms 111\u003c\/p\u003e \u003cp\u003e2.9.1 Discrete Fourier transform (DFT) 111\u003c\/p\u003e \u003cp\u003e2.9.2 Canvas support weave analysis 114\u003c\/p\u003e \u003cp\u003e2.9.3 Discrete wavelet transform (DWT) 116\u003c\/p\u003e \u003cp\u003e2.10 Compositing and integrating art images 118\u003c\/p\u003e \u003cp\u003e2.10.1 Image compositing 118\u003c\/p\u003e \u003cp\u003e2.10.2 Superresolution 119\u003c\/p\u003e \u003cp\u003e2.11 Image separation 123\u003c\/p\u003e \u003cp\u003e2.12 Summary 123\u003c\/p\u003e \u003cp\u003e2.13 Bibliographical remarks 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Color analysis 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 129\u003c\/p\u003e \u003cp\u003e3.2 Visible–light spectra and color appearance 132\u003c\/p\u003e \u003cp\u003e3.3 Overview of human color vision 133\u003c\/p\u003e \u003cp\u003e3.3.1 Properties of color descriptions 134\u003c\/p\u003e \u003cp\u003e3.3.2 Opponent color processing and unique hues 137\u003c\/p\u003e \u003cp\u003e3.3.3 Humanist descriptions of color 138\u003c\/p\u003e \u003cp\u003e3.3.4 Spatial aspects of color perception 139\u003c\/p\u003e \u003cp\u003eJosef Albers 140\u003c\/p\u003e \u003cp\u003e3.3.5 Color and lightness constancy and brightness perception 141\u003c\/p\u003e \u003cp\u003e3.3.6 Quantitative descriptions and additive color mixing 141\u003c\/p\u003e \u003cp\u003e3.3.7 Representing artists' palettes 145\u003c\/p\u003e \u003cp\u003e3.4 Physics of color in art materials 147\u003c\/p\u003e \u003cp\u003e3.4.1 Pigments and color appearance 147\u003c\/p\u003e \u003cp\u003e3.5 Representing color arising from mixing paints 151\u003c\/p\u003e \u003cp\u003e3.5.1 Identifying pigments in artworks based on spectra 152\u003c\/p\u003e \u003cp\u003e3.6 Digital rejuvenation of pigment colors 154\u003c\/p\u003e \u003cp\u003e3.6.1 Digital rejuvenation of faded artworks 157\u003c\/p\u003e \u003cp\u003eGeorges Seurat 158\u003c\/p\u003e \u003cp\u003e3.7 Digital cleaning of paintings 160\u003c\/p\u003e \u003cp\u003e3.8 Summary 164\u003c\/p\u003e \u003cp\u003e3.9 Bibliographical remarks 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Brush stroke and mark analysis 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 171\u003c\/p\u003e \u003cp\u003eCy Twombly 173\u003c\/p\u003e \u003cp\u003e4.2 Analysis of printed lines and marks 175\u003c\/p\u003e \u003cp\u003eKatsushika Hokusai 178\u003c\/p\u003e \u003cp\u003e4.3 Inferring tools from marks 182\u003c\/p\u003e \u003cp\u003eSheila Waters 184\u003c\/p\u003e \u003cp\u003e4.3.1 Analysis of brush strokes 185\u003c\/p\u003e \u003cp\u003e4.3.2 Segmenting and isolating brush strokes computationally 187\u003c\/p\u003e \u003cp\u003e4.3.3 Extracting opaque marks in multiple layers 189\u003c\/p\u003e \u003cp\u003eVincent Willem van Gogh 193\u003c\/p\u003e \u003cp\u003e4.3.4 Visual evidence of authorship of Pollock's drip paintings 194\u003c\/p\u003e \u003cp\u003eJackson Pollock 195\u003c\/p\u003e \u003cp\u003e4.3.5 Extracting layers of translucent brush strokes 195\u003c\/p\u003e \u003cp\u003e4.4 Characterizing the shapes of strokes and marks 203\u003c\/p\u003e \u003cp\u003e4.5 Global methods for inferring sequences of marks in paintings 206\u003c\/p\u003e \u003cp\u003e4.6 Summary 208\u003c\/p\u003e \u003cp\u003e4.7 Bibliographical remarks 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Perspective and geometric analysis 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 211\u003c\/p\u003e \u003cp\u003e5.2 Projective geometry 214\u003c\/p\u003e \u003cp\u003e5.2.1 The mathematics of projection 216\u003c\/p\u003e \u003cp\u003e5.2.2 One–point, two–point, and three–point perspectives 222\u003c\/p\u003e \u003cp\u003e5.2.3 Parallel or orthographic perspective in Asian art 223\u003c\/p\u003e \u003cp\u003e5.3 Estimating the center of projection 224\u003c\/p\u003e \u003cp\u003e5.3.1 Foreshortening and size comparisons of depicted objects 230\u003c\/p\u003e \u003cp\u003ePiero della Francesca 231\u003c\/p\u003e \u003cp\u003e5.3.2 Cross–ratio analysis 232\u003c\/p\u003e \u003cp\u003e5.3.3 Estimating the center of projection from object sizes 234\u003c\/p\u003e \u003cp\u003e5.4 Estimating geometric accuracy in artworks 235\u003c\/p\u003e \u003cp\u003e5.4.1 Hans Memling's Flower Still-Life 235\u003c\/p\u003e \u003cp\u003eHans Memling 237\u003c\/p\u003e \u003cp\u003e5.4.2 The carpet in Lorenzo Lotto's Husband and Wife 238\u003c\/p\u003e \u003cp\u003e5.4.3 The chandelier in the Arnolfini Portrait 238\u003c\/p\u003e \u003cp\u003eJan van Eyck 243\u003c\/p\u003e \u003cp\u003e5.4.4 Warping Andrea Mantegna's Lamentation of Christ to make consistent perspective 251\u003c\/p\u003e \u003cp\u003e5.4.5 Dewarping the murals in Sennedjem's Tomb 252\u003c\/p\u003e \u003cp\u003e5.4.6 Warping de Chirico's Ariadne to make consistent perspective 255\u003c\/p\u003e \u003cp\u003eGiorgio de Chirico 256\u003c\/p\u003e \u003cp\u003e5.4.7 Robert Campin and workshop's Mérode Altarpiece 257\u003c\/p\u003e \u003cp\u003eRobert Campin 258\u003c\/p\u003e \u003cp\u003e5.5 Slant anamorphic art 260\u003c\/p\u003e \u003cp\u003eEd Ruscha (Edward Joseph Ruscha IV) 260\u003c\/p\u003e \u003cp\u003e5.5.1 Hans Holbein's The Ambassadors 263\u003c\/p\u003e \u003cp\u003eHans Holbein 263\u003c\/p\u003e \u003cp\u003e5.6 Inferring depth from projected images 264\u003c\/p\u003e \u003cp\u003e5.6.1 Computing a three–dimensional model from one perspective image 265\u003c\/p\u003e \u003cp\u003eMasaccio 266\u003c\/p\u003e \u003cp\u003e5.6.2 Computing a three–dimensional model from two perspective images 267\u003c\/p\u003e \u003cp\u003e5.7 Summary 271\u003c\/p\u003e \u003cp\u003e5.8 Bibliographical remarks 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Optical analysis 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 275\u003c\/p\u003e \u003cp\u003e6.2 Reflection and refraction 277\u003c\/p\u003e \u003cp\u003e6.3 Plane mirrors 278\u003c\/p\u003e \u003cp\u003e6.3.1 Virtual image formation by plane mirrors 279\u003c\/p\u003e \u003cp\u003e6.3.2 Depictions of plane mirrors in art 281\u003c\/p\u003e \u003cp\u003e6.3.3 Diego Velázquez’s Las Meninas 283\u003c\/p\u003e \u003cp\u003eDiego Velázquez 284\u003c\/p\u003e \u003cp\u003e6.4 Convex spherical mirrors 288\u003c\/p\u003e \u003cp\u003e6.4.1 Virtual image formation by convex spherical mirrors 290\u003c\/p\u003e \u003cp\u003e6.4.2 Jan van Eyck’s Portrait of Giovanni Arnolfini and his Wife 292\u003c\/p\u003e \u003cp\u003e6.4.3 Claude glass 297\u003c\/p\u003e \u003cp\u003e6.4.4 Parmigianino’s Self–Portrait in a Convex Mirror 298\u003c\/p\u003e \u003cp\u003eParmigianino (Girolamo Francesco Maria Mazzola) 298\u003c\/p\u003e \u003cp\u003e6.4.5 Hans Memling's Virgin and Child and Maarten van Nieuwenhove 304\u003c\/p\u003e \u003cp\u003e6.4.6 Dewarping images in generalized cylindrical mirrors 308\u003c\/p\u003e \u003cp\u003e6.5 Conical and cylindrical mirrors and anamorphic art 312\u003c\/p\u003e \u003cp\u003e6.5.1 Conical mirror anamorphic art 313\u003c\/p\u003e \u003cp\u003e6.5.2 Cylindrical mirror anamorphic art 317\u003c\/p\u003e \u003cp\u003e6.6 Concave spherical mirrors 318\u003c\/p\u003e \u003cp\u003e6.6.1 Virtual image formation by concave mirrors 320\u003c\/p\u003e \u003cp\u003e6.6.2 Real image formation by concave mirrors 322\u003c\/p\u003e \u003cp\u003e6.7 Converging lenses 323\u003c\/p\u003e \u003cp\u003e6.7.1 Virtual image formation by converging lenses 325\u003c\/p\u003e \u003cp\u003e6.7.2 Real image formation by convex lenses 327\u003c\/p\u003e \u003cp\u003e6.8 Camera lucida and camera obscura 328\u003c\/p\u003e \u003cp\u003e6.8.1 Camera lucida 328\u003c\/p\u003e \u003cp\u003e6.8.2 Camera obscura 331\u003c\/p\u003e \u003cp\u003e6.8.3 Depth of field, depth of focus, and blur spots 333\u003c\/p\u003e \u003cp\u003e6.9 Optical projections and the creation of art 336\u003c\/p\u003e \u003cp\u003e6.9.1 Jan van Eyck's Portrait of Giovanni Arnolfini and his wife 337\u003c\/p\u003e \u003cp\u003e6.9.2 Caravaggio's Supper at Emmaus 342\u003c\/p\u003e \u003cp\u003e6.9.3 Lorenzo Lotto's Husband and Wife 345\u003c\/p\u003e \u003cp\u003e6.9.4 Johannes Vermeer's Lady at the Virginals with a Gentleman 349\u003c\/p\u003e \u003cp\u003eJohannes Vermeer 349\u003c\/p\u003e \u003cp\u003e6.9.5 Canaletto's Piazza San Marco 363\u003c\/p\u003e \u003cp\u003eCanaletto (Giovanni Antonio Canal) 364\u003c\/p\u003e \u003cp\u003e6.9.6 Photorealists 364\u003c\/p\u003e \u003cp\u003ePhilip Barlow 366\u003c\/p\u003e \u003cp\u003e6.10 Refraction and nonimaging optics in art 366\u003c\/p\u003e \u003cp\u003e6.10.1 Leonardo's Salvator Mundi 366\u003c\/p\u003e \u003cp\u003e6.11 Summary 371\u003c\/p\u003e \u003cp\u003e6.12 Bibliographical remarks 372\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Lighting analysis 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 377\u003c\/p\u003e \u003cp\u003e7.2 Basic shadows 381\u003c\/p\u003e \u003cp\u003e7.2.1 General classes of lighting analysis methods 383\u003c\/p\u003e \u003cp\u003e7.3 Cast–shadow analysis 383\u003c\/p\u003e \u003cp\u003e7.3.1 Illumination from two or more point-sources 388\u003c\/p\u003e \u003cp\u003e7.3.2 Cast–shadow analysis under geometric constraints 388\u003c\/p\u003e \u003cp\u003e7.4 Lighting information from highlights 389\u003c\/p\u003e \u003cp\u003e7.4.1 Illumination direction from highlights on simple estimated shapes 393\u003c\/p\u003e \u003cp\u003e7.5 The optics of diffuse reflections 394\u003c\/p\u003e \u003cp\u003e7.6 Inferring illumination from plane surfaces 396\u003c\/p\u003e \u003cp\u003eGeorges de la Tour 398\u003c\/p\u003e \u003cp\u003e7.7 Interreflection 400\u003c\/p\u003e \u003cp\u003e7.8 Occluding–contour algorithms 401\u003c\/p\u003e \u003cp\u003e7.8.1 Single–point occluding–contour algorithm 403\u003c\/p\u003e \u003cp\u003e7.8.2 General occluding–contour algorithm 405\u003c\/p\u003e \u003cp\u003eCaravaggio (Michelangelo Merisi da Caravaggio) 407\u003c\/p\u003e \u003cp\u003e7.8.3 Lightfield occluding–contour algorithm 408\u003c\/p\u003e \u003cp\u003eGarth Herrick 409\u003c\/p\u003e \u003cp\u003e7.8.4 Theory of the lightfield occluding–contour algorithm 410\u003c\/p\u003e \u003cp\u003e7.8.5 Application of the lightfield occluding–contour algorithm 415\u003c\/p\u003e \u003cp\u003e7.9 Computer graphics for the analysis of lighting 418\u003c\/p\u003e \u003cp\u003e7.9.1 Georges de la Tour's Christ in the Carpenter's Studio (model) 419\u003c\/p\u003e \u003cp\u003e7.9.2 Johannes Vermeer's Girl with a Pearl Earring 421\u003c\/p\u003e \u003cp\u003e7.9.3 René Magritte's The Menaced Assassin 422\u003c\/p\u003e \u003cp\u003e7.9.4 Bidirectional reflectance distribution functions (BRDFs) 424\u003c\/p\u003e \u003cp\u003e7.9.5 Caravaggio's The Calling of St. Matthew 425\u003c\/p\u003e \u003cp\u003e7.10 Shape–from–shading algorithms 426\u003c\/p\u003e \u003cp\u003e7.10.1 Shape–from–shading by deep neural networks 429\u003c\/p\u003e \u003cp\u003e7.10.2 Shape–from–shading for estimating both illumination and depth 430\u003c\/p\u003e \u003cp\u003e7.11 Integrating lighting estimates 433\u003c\/p\u003e \u003cp\u003e7.11.1 Integrating one–dimensional lighting estimates 433\u003c\/p\u003e \u003cp\u003e7.11.2 Integrating two–dimensional lighting estimates 436\u003c\/p\u003e \u003cp\u003e7.12 Lighting analysis for dating depicted scenes 439\u003c\/p\u003e \u003cp\u003e7.13 Summary 442\u003c\/p\u003e \u003cp\u003e7.14 Bibliographical remarks 444\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Object analysis 449\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 449\u003c\/p\u003e \u003cp\u003e8.2 Image–based object classification 452\u003c\/p\u003e \u003cp\u003e8.2.1 Feature–based object recognition 452\u003c\/p\u003e \u003cp\u003e8.3 Feature–based analysis of faces and bodies 454\u003c\/p\u003e \u003cp\u003e8.3.1 Feature–based analysis of body pose 464\u003c\/p\u003e \u003cp\u003e8.3.2 Feature–based analysis of head poses 466\u003c\/p\u003e \u003cp\u003e8.4 Deep neural network–based object recognition 468\u003c\/p\u003e \u003cp\u003eJacques-Louis David 472\u003c\/p\u003e \u003cp\u003e8.4.1 Transfer training 472\u003c\/p\u003e \u003cp\u003e8.5 Summary 474\u003c\/p\u003e \u003cp\u003e8.6 Bibliographical remarks 475\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Style and composition analysis 477\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 477\u003c\/p\u003e \u003cp\u003e9.2 Automatic classification of style 480\u003c\/p\u003e \u003cp\u003e9.3 Compositional balance 482\u003c\/p\u003e \u003cp\u003e9.3.1 Computational balance of actors 485\u003c\/p\u003e \u003cp\u003e9.4 Geometric properties of composition 486\u003c\/p\u003e \u003cp\u003e9.4.1 Design in Piet Mondrian's Neoplastic paintings 487\u003c\/p\u003e \u003cp\u003ePiet Mondrian 487\u003c\/p\u003e \u003cp\u003e9.5 Analysis of trends and similarities in artistic style 497\u003c\/p\u003e \u003cp\u003e9.5.1 Trends in landscape compositions 498\u003c\/p\u003e \u003cp\u003e9.5.2 Large–scale trends in the development of style 502\u003c\/p\u003e \u003cp\u003e9.5.3 Graph representations of stylistic similarities 503\u003c\/p\u003e \u003cp\u003e9.6 Style transfer 505\u003c\/p\u003e \u003cp\u003e9.6.1 Style transfer by deep networks 505\u003c\/p\u003e \u003cp\u003e9.6.2 Rejuvenating tapestries 506\u003c\/p\u003e \u003cp\u003e9.6.3 Coloration of black–and–white photographs of artworks 507\u003c\/p\u003e \u003cp\u003e9.6.4 Style transfer for visualizing underdrawings 509\u003c\/p\u003e \u003cp\u003e9.7 Recovering Rembrandt's complete The Night Watch 513\u003c\/p\u003e \u003cp\u003eRembrandt 514\u003c\/p\u003e \u003cp\u003e9.8 Computational generation of images for art analysis 516\u003c\/p\u003e \u003cp\u003e9.8.1 Computational recovery of lost artworks 518\u003c\/p\u003e \u003cp\u003e9.9 Summary 521\u003c\/p\u003e \u003cp\u003e9.10 Bibliographical remarks 522\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Semantic analysis 525\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 525\u003c\/p\u003e \u003cp\u003eJacques-Louis David 528\u003c\/p\u003e \u003cp\u003e10.2 Semantics and visual art 534\u003c\/p\u003e \u003cp\u003e10.2.1 Natural language processing and knowledge representation 536\u003c\/p\u003e \u003cp\u003e10.3 Meaning through associations 538\u003c\/p\u003e \u003cp\u003e10.3.1 Signifiers and signifieds 538\u003c\/p\u003e \u003cp\u003e10.4 Semantics of color 544\u003c\/p\u003e \u003cp\u003e10.5 Identifying saints by their attributes 546\u003c\/p\u003e \u003cp\u003eAndrea del Verrocchio 549\u003c\/p\u003e \u003cp\u003e10.6 Learning associations between signifiers and signifieds 550\u003c\/p\u003e \u003cp\u003eHarmen Steenwijck 551\u003c\/p\u003e \u003cp\u003e10.7 Meaning through artistic style 554\u003c\/p\u003e \u003cp\u003e10.7.1 Context in the creation of meaning 556\u003c\/p\u003e \u003cp\u003e10.8 Automatic image captioning and question answering 557\u003c\/p\u003e \u003cp\u003e10.8.1 Image captioning 557\u003c\/p\u003e \u003cp\u003e10.8.2 Automatic answering of questions about artworks 559\u003c\/p\u003e \u003cp\u003e10.9 Meaning through shape relations and associations 563\u003c\/p\u003e \u003cp\u003eRogier van der Weyden 563\u003c\/p\u003e \u003cp\u003e10.9.1 Recognizing meaning–bearing stories 565\u003c\/p\u003e \u003cp\u003eAlbrecht Dürer 567\u003c\/p\u003e \u003cp\u003e10.10 Summary 568\u003c\/p\u003e \u003cp\u003e10.11 Bibliographical remarks 569\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix 573\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Symbols, acronyms, and mathematical notation 573\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Mathematical notation, definitions, and operations 573\u003c\/p\u003e \u003cp\u003eA.2 Solving simultaneous linear equations 578\u003c\/p\u003e \u003cp\u003eA.3 Lagrange optimization 579\u003c\/p\u003e \u003cp\u003eA.4 Basis functions 580\u003c\/p\u003e \u003cp\u003eA.5 Discrete Fourier analysis and synthesis 580\u003c\/p\u003e \u003cp\u003eA.6 Discrete wavelet transform 582\u003c\/p\u003e \u003cp\u003eA.7 Spherical harmonics 582\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Probability 584\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Accuracy, precision, and recall 585\u003c\/p\u003e \u003cp\u003eB.2 Conditional probability 585\u003c\/p\u003e \u003cp\u003eB.3 The definition of information 586\u003c\/p\u003e \u003cp\u003eB.4 Hidden Markov models (HMMs) 586\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Bayes' theorem and reasoning about uncertainty 588\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 Statistical independence 588\u003c\/p\u003e \u003cp\u003eC.2 Maximum likelihood estimation 589\u003c\/p\u003e \u003cp\u003eC.3 Bias and variance 591\u003c\/p\u003e \u003cp\u003eC.4 Intersection over Union metric 592\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Deep neural networks 593\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eE Ray tracing and image formation in mirrors and lenses 596\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eE.1 Converging lenses 596\u003c\/p\u003e \u003cp\u003eE.2 Diverging lenses 599\u003c\/p\u003e \u003cp\u003eE.3 Mirrors 600\u003c\/p\u003e \u003cp\u003eE.4 The focal length and radius of curvature of a spherical mirror 602\u003c\/p\u003e \u003cp\u003eE.5 Spherical versus parabolic mirrors 603\u003c\/p\u003e \u003cp\u003e\u003cb\u003eF Resources 604\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEpilog 607\u003c\/p\u003e \u003cp\u003eGlossary 609\u003c\/p\u003e \u003cp\u003eBibliography 615\u003c\/p\u003e \u003cp\u003eFigure credits 673\u003c\/p\u003e \u003cp\u003eTimeline of artists 682\u003c\/p\u003e \u003cp\u003eIndex of artists 683\u003c\/p\u003e \u003cp\u003eIndex 687\u003c\/p\u003e \u003cp\u003eAbout the book 713\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.\u003c\/p\u003e\u003cp\u003eMore than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook\/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.\u003c\/p\u003e\u003cp\u003eTopics and features:\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eStructured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses\u003c\/li\u003e\n\u003cli\u003eIncorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality\u003c\/li\u003e\n\u003cli\u003ePresents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eIncludes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eProvides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eSuitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. 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It provides a complete, state-of-the-art review of the collective experience that the SHM community has gained in recent years. It also extensively explores the potentials of the vision sensor as a fast and cost-effective tool for solving SHM problems based on both time and frequency domain analytics, broadening the application of emerging computer vision sensor technology in not only scientific research but also engineering practice.    Computer Vision for Structural Dynamics and Health Monitoring presents fundamental knowledge, important issues, and practical techniques critical to successful development of vision-based sensors in detail, including robustness of template matching techniques for t\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Figures ix\u003c\/p\u003e \u003cp\u003eList of Tables xv\u003c\/p\u003e \u003cp\u003eSeries Preface xvii\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Structural Health Monitoring: A Quick Review 1\u003c\/p\u003e \u003cp\u003e1.2 Computer Vision Sensors for Structural Health Monitoring 3\u003c\/p\u003e \u003cp\u003e1.3 Organization of the Book 7\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Development of a Computer Vision Sensor for Structural Displacement Measurement 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Vision Sensor System Hardware 11\u003c\/p\u003e \u003cp\u003e2.2 Vision Sensor System Software: Template-Matching Techniques 15\u003c\/p\u003e \u003cp\u003e2.2.1 Area-Based Template Matching 16\u003c\/p\u003e \u003cp\u003e2.2.2 Feature-Based Template Matching 20\u003c\/p\u003e \u003cp\u003e2.3 Coordinate Conversion and Scaling Factors 22\u003c\/p\u003e \u003cp\u003e2.3.1 Camera Calibration Method 23\u003c\/p\u003e \u003cp\u003e2.3.2 Practical Calibration Method 25\u003c\/p\u003e \u003cp\u003e2.4 Representative Template Matching Algorithms 28\u003c\/p\u003e \u003cp\u003e2.4.1 Intensity-Based UCC Technique 28\u003c\/p\u003e \u003cp\u003e2.4.2 Gradient-Based Robust OCM Technique 33\u003c\/p\u003e \u003cp\u003e2.4.3 Vision Sensor Software Package and Operation 39\u003c\/p\u003e \u003cp\u003e2.5 Summary 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Performance Evaluation Through Laboratory and Field Tests 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Seismic Shaking Table Test 43\u003c\/p\u003e \u003cp\u003e3.2 Shaking Table Test of Frame Structure 1 46\u003c\/p\u003e \u003cp\u003e3.2.1 Test Description 46\u003c\/p\u003e \u003cp\u003e3.2.2 Subpixel Resolution 47\u003c\/p\u003e \u003cp\u003e3.2.3 Performance When Tracking Artificial Targets 48\u003c\/p\u003e \u003cp\u003e3.2.4 Performance When Tracking Natural Targets 49\u003c\/p\u003e \u003cp\u003e3.2.5 Error Quantification 51\u003c\/p\u003e \u003cp\u003e3.2.6 Evaluation of OCM and UCC Robustness 51\u003c\/p\u003e \u003cp\u003e3.3 Seismic Shaking Table Test of Frame Structure 2 56\u003c\/p\u003e \u003cp\u003e3.4 Free Vibration Test of a Beam Structure 59\u003c\/p\u003e \u003cp\u003e3.4.1 Test Description 59\u003c\/p\u003e \u003cp\u003e3.4.2 Evaluation of the Practical Calibration Method 60\u003c\/p\u003e \u003cp\u003e3.5 Field Test of a Pedestrian Bridge 63\u003c\/p\u003e \u003cp\u003e3.6 Field Test of a Highway Bridge 66\u003c\/p\u003e \u003cp\u003e3.7 Field Test of Two Railway Bridges 67\u003c\/p\u003e \u003cp\u003e3.7.1 Test Description 69\u003c\/p\u003e \u003cp\u003e3.7.2 Daytime Measurements 72\u003c\/p\u003e \u003cp\u003e3.7.3 Nighttime Measurements 72\u003c\/p\u003e \u003cp\u003e3.7.4 Field Performance Evaluation 75\u003c\/p\u003e \u003cp\u003e3.8 Remote Measurement of the Vincent Thomas Bridge 81\u003c\/p\u003e \u003cp\u003e3.9 Remote Measurement of the Manhattan Bridge 82\u003c\/p\u003e \u003cp\u003e3.10 Summary 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Application in Modal Analysis, Model Updating, and Damage Detection 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Experimental Modal Analysis 91\u003c\/p\u003e \u003cp\u003e4.1.1 Modal Analysis of a Frame 91\u003c\/p\u003e \u003cp\u003e4.1.2 Modal Analysis of a Beam 97\u003c\/p\u003e \u003cp\u003e4.2 Model Updating as a Frequency-Domain Optimization Problem 101\u003c\/p\u003e \u003cp\u003e4.3 Damage Detection 108\u003c\/p\u003e \u003cp\u003e4.3.1 Mode Shape Curvature-Based Damage Index 108\u003c\/p\u003e \u003cp\u003e4.3.2 Test Description 109\u003c\/p\u003e \u003cp\u003e4.3.3 Damage Detection Results 110\u003c\/p\u003e \u003cp\u003e4.4 Summary 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Application in Model Updating of Railway Bridges under Trainloads 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Field Measurement of Bridge Displacement under Trainloads 116\u003c\/p\u003e \u003cp\u003e5.2 Formulation of the Finite Element Model 118\u003c\/p\u003e \u003cp\u003e5.2.1 Modeling the Train-Track-Bridge Interaction 118\u003c\/p\u003e \u003cp\u003e5.2.2 Finite Element Model of the Railway Bridge 120\u003c\/p\u003e \u003cp\u003e5.3 Sensitivity Analysis and Finite Element Model Updating 121\u003c\/p\u003e \u003cp\u003e5.3.1 Model Updating as a Time-Domain Optimization Problem 122\u003c\/p\u003e \u003cp\u003e5.3.2 Sensitivity Analysis of Displacement and Acceleration Responses 123\u003c\/p\u003e \u003cp\u003e5.3.3 Finite Element Model Updating 127\u003c\/p\u003e \u003cp\u003e5.4 Dynamic Characteristics of Short-Span Bridges under Trainloads 130\u003c\/p\u003e \u003cp\u003e5.5 Summary 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Application in Simultaneously Identifying Structural Parameters and Excitation Forces 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Simultaneous Identification Using Vision-Based Displacement Measurements 140\u003c\/p\u003e \u003cp\u003e6.1.1 Structural Parameter Identification as a Time-Domain Optimization Problem 141\u003c\/p\u003e \u003cp\u003e6.1.2 Force Identification Based on Structural Displacement Measurements 142\u003c\/p\u003e \u003cp\u003e6.1.3 Simultaneous Identification Procedure 144\u003c\/p\u003e \u003cp\u003e6.2 Numerical Example 146\u003c\/p\u003e \u003cp\u003e6.2.1 Robustness to Noise and Number of Sensors 147\u003c\/p\u003e \u003cp\u003e6.2.2 Robustness to Initial Stiffness Values 150\u003c\/p\u003e \u003cp\u003e6.2.3 Robustness to Damping Ratio Values 150\u003c\/p\u003e \u003cp\u003e6.3 Experimental Validation 154\u003c\/p\u003e \u003cp\u003e6.3.1 Test Description 154\u003c\/p\u003e \u003cp\u003e6.3.2 Identification Results 155\u003c\/p\u003e \u003cp\u003e6.4 Summary 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Application in Estimating Cable Force 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Vision Sensor for Estimating Cable Force 172\u003c\/p\u003e \u003cp\u003e7.1.1 Vibration Method 172\u003c\/p\u003e \u003cp\u003e7.1.2 Procedure for Vision-Based Cable Tension Estimation 173\u003c\/p\u003e \u003cp\u003e7.2 Implementation in the Hard Rock Stadium Renovation Project 174\u003c\/p\u003e \u003cp\u003e7.2.1 Hard Rock Stadium 175\u003c\/p\u003e \u003cp\u003e7.2.2 Test Description 176\u003c\/p\u003e \u003cp\u003e7.2.3 Estimating and Validating Cable Force 178\u003c\/p\u003e \u003cp\u003e7.3 Implementation in the Bronx-Whitestone Bridge Suspender Replacement Project 184\u003c\/p\u003e \u003cp\u003e7.3.1 Bronx-Whitestone Bridge 184\u003c\/p\u003e \u003cp\u003e7.3.2 Estimating Suspender Tension 185\u003c\/p\u003e \u003cp\u003e7.4 Summary 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Achievements, Challenges, and Opportunities 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Capabilities of Vision-Based Displacement Sensors: A Summary 191\u003c\/p\u003e \u003cp\u003e8.1.1 Artificial vs. Natural Targets 192\u003c\/p\u003e \u003cp\u003e8.1.2 Single-Point vs. Multipoint Measurements 192\u003c\/p\u003e \u003cp\u003e8.1.3 Pixel vs. Subpixel Resolution 193\u003c\/p\u003e \u003cp\u003e8.1.4 2D vs. 3D Measurements 194\u003c\/p\u003e \u003cp\u003e8.1.5 Real Time vs. Post Processing 194\u003c\/p\u003e \u003cp\u003e8.2 Sources of Error in Vision-Based Displacement Sensors 195\u003c\/p\u003e \u003cp\u003e8.2.1 Camera Motion 196\u003c\/p\u003e \u003cp\u003e8.2.2 Coordinate Conversion 197\u003c\/p\u003e \u003cp\u003e8.2.3 Hardware Limitations 198\u003c\/p\u003e \u003cp\u003e8.2.4 Environmental Sources 198\u003c\/p\u003e \u003cp\u003e8.3 Vision-Based Displacement Sensors for Structural Health Monitoring 199\u003c\/p\u003e \u003cp\u003e8.3.1 Dynamic Displacement Measurement 199\u003c\/p\u003e \u003cp\u003e8.3.2 Modal Property Identification 201\u003c\/p\u003e \u003cp\u003e8.3.3 Model Updating and Damage Detection 202\u003c\/p\u003e \u003cp\u003e8.3.4 Cable Force Estimation 203\u003c\/p\u003e \u003cp\u003e8.4 Other Civil and Structural Engineering Applications 204\u003c\/p\u003e \u003cp\u003e8.4.1 Automated Machine Visual Inspection 204\u003c\/p\u003e \u003cp\u003e8.4.2 Onsite Construction Tracking and Safety Monitoring 206\u003c\/p\u003e \u003cp\u003e8.4.3 Vehicle Load Estimation 206\u003c\/p\u003e \u003cp\u003e8.4.4 Other Applications 207\u003c\/p\u003e \u003cp\u003e8.5 Future Research Directions 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix: Fundamentals of Digital Image Processing Using MATLAB 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Digital Image Representation 211\u003c\/p\u003e \u003cp\u003eA.2 Noise Removal 214\u003c\/p\u003e \u003cp\u003eA.3 Edge Detection 216\u003c\/p\u003e \u003cp\u003eA.4 Discrete Fourier Transform 217\u003c\/p\u003e \u003cp\u003eReferences 221\u003c\/p\u003e \u003cp\u003eIndex 229\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49371825799511,"sku":"9781119566588","price":100.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119566588.jpg?v=1730154712"},{"product_id":"artificial-intelligence-for-virtual-reality-9783110713749","title":"Artificial Intelligence for Virtual Reality","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book explores the possible applications of Artificial Intelligence in Virtual environments. 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Social Learning provides a comprehensive approach to researching methods in the emerging fields of AR\/VR. The contributors of this book outline the state-of-the-art implementation of AR\/VR for the Internet of Things, Blockchains, Big Data, and 5G within AR\/VR systems.","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":49372696478039,"sku":"9783110994926","price":123.5,"currency_code":"GBP","in_stock":true}]},{"product_id":"an-introduction-to-3d-computer-vision-techniques-and-algorithms-9780470017043","title":"An Introduction to 3D Computer Vision Techniques","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eComputer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“This text is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 2012)\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e \u003cbr\u003e \u003cbr\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgements xvii\u003c\/p\u003e \u003cp\u003eNotation and Abbreviations xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Stereo-pair Images and Depth Perception 4\u003c\/p\u003e \u003cp\u003e1.2 3D Vision Systems 4\u003c\/p\u003e \u003cp\u003e1.3 3D Vision Applications 5\u003c\/p\u003e \u003cp\u003e1.4 Contents Overview: The 3D Vision Task in Stages 6\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Brief History of Research on Vision 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Abstract 9\u003c\/p\u003e \u003cp\u003e2.2 Retrospective of Vision Research 9\u003c\/p\u003e \u003cp\u003e2.3 Closure 14\u003c\/p\u003e \u003cp\u003e2.3.1 Further Reading 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 2D and 3D Vision Formation 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Abstract 17\u003c\/p\u003e \u003cp\u003e3.2 Human Visual System 18\u003c\/p\u003e \u003cp\u003e3.3 Geometry and Acquisition of a Single Image 23\u003c\/p\u003e \u003cp\u003e3.3.1 Projective Transformation 24\u003c\/p\u003e \u003cp\u003e3.3.2 Simple Camera System: the Pin-hole Model 24\u003c\/p\u003e \u003cp\u003e3.3.3 Projective Transformation of the Pin-hole Camera 28\u003c\/p\u003e \u003cp\u003e3.3.4 Special Camera Setups 29\u003c\/p\u003e \u003cp\u003e3.3.5 Parameters of Real Camera Systems 30\u003c\/p\u003e \u003cp\u003e3.4 Stereoscopic Acquisition Systems 31\u003c\/p\u003e \u003cp\u003e3.4.1 Epipolar Geometry 31\u003c\/p\u003e \u003cp\u003e3.4.2 Canonical Stereoscopic System 36\u003c\/p\u003e \u003cp\u003e3.4.3 Disparity in the General Case 38\u003c\/p\u003e \u003cp\u003e3.4.4 Bifocal, Trifocal and Multifocal Tensors 39\u003c\/p\u003e \u003cp\u003e3.4.5 Finding the Essential and Fundamental Matrices 41\u003c\/p\u003e \u003cp\u003e3.4.6 Dealing with Outliers 49\u003c\/p\u003e \u003cp\u003e3.4.7 Catadioptric Stereo Systems 54\u003c\/p\u003e \u003cp\u003e3.4.8 Image Rectification 55\u003c\/p\u003e \u003cp\u003e3.4.9 Depth Resolution in Stereo Setups 59\u003c\/p\u003e \u003cp\u003e3.4.10 Stereo Images and Reference Data 61\u003c\/p\u003e \u003cp\u003e3.5 Stereo Matching Constraints 66\u003c\/p\u003e \u003cp\u003e3.6 Calibration of Cameras 70\u003c\/p\u003e \u003cp\u003e3.6.1 Standard Calibration Methods 71\u003c\/p\u003e \u003cp\u003e3.6.2 Photometric Calibration 73\u003c\/p\u003e \u003cp\u003e3.6.3 Self-calibration 73\u003c\/p\u003e \u003cp\u003e3.6.4 Calibration of the Stereo Setup 74\u003c\/p\u003e \u003cp\u003e3.7 Practical Examples 75\u003c\/p\u003e \u003cp\u003e3.7.1 Image Representation and Basic Structures 75\u003c\/p\u003e \u003cp\u003e3.8 Appendix: Derivation of the Pin-hole Camera Transformation 91\u003c\/p\u003e \u003cp\u003e3.9 Closure 93\u003c\/p\u003e \u003cp\u003e3.9.1 Further Reading 93\u003c\/p\u003e \u003cp\u003e3.9.2 Problems and Exercises 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Low-level Image Processing for Image Matching 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Abstract 95\u003c\/p\u003e \u003cp\u003e4.2 Basic Concepts 95\u003c\/p\u003e \u003cp\u003e4.2.1 Convolution and Filtering 95\u003c\/p\u003e \u003cp\u003e4.2.2 Filter Separability 97\u003c\/p\u003e \u003cp\u003e4.3 Discrete Averaging 99\u003c\/p\u003e \u003cp\u003e4.3.1 Gaussian Filter 100\u003c\/p\u003e \u003cp\u003e4.3.2 Binomial Filter 101\u003c\/p\u003e \u003cp\u003e4.4 Discrete Differentiation 105\u003c\/p\u003e \u003cp\u003e4.4.1 Optimized Differentiating Filters 105\u003c\/p\u003e \u003cp\u003e4.4.2 Savitzky–Golay Filters 108\u003c\/p\u003e \u003cp\u003e4.5 Edge Detection 115\u003c\/p\u003e \u003cp\u003e4.5.1 Edges from Signal Gradient 117\u003c\/p\u003e \u003cp\u003e4.5.2 Edges from the Savitzky–Golay Filter 119\u003c\/p\u003e \u003cp\u003e4.5.3 Laplacian of Gaussian 120\u003c\/p\u003e \u003cp\u003e4.5.4 Difference of Gaussians 126\u003c\/p\u003e \u003cp\u003e4.5.5 Morphological Edge Detector 127\u003c\/p\u003e \u003cp\u003e4.6 Structural Tensor 127\u003c\/p\u003e \u003cp\u003e4.6.1 Locally Oriented Neighbourhoods in Images 128\u003c\/p\u003e \u003cp\u003e4.6.2 Tensor Representation of Local Neighbourhoods 133\u003c\/p\u003e \u003cp\u003e4.6.3 Multichannel Image Processing with Structural Tensor 143\u003c\/p\u003e \u003cp\u003e4.7 Corner Detection 144\u003c\/p\u003e \u003cp\u003e4.7.1 The Most Common Corner Detectors 144\u003c\/p\u003e \u003cp\u003e4.7.2 Corner Detection with the Structural Tensor 149\u003c\/p\u003e \u003cp\u003e4.8 Practical Examples 151\u003c\/p\u003e \u003cp\u003e4.8.1 C++ Implementations 151\u003c\/p\u003e \u003cp\u003e4.8.2 Implementation of the Morphological Operators 157\u003c\/p\u003e \u003cp\u003e4.8.3 Examples in Matlab: Computation of the SVD 161\u003c\/p\u003e \u003cp\u003e4.9 Closure 162\u003c\/p\u003e \u003cp\u003e4.9.1 Further Reading 163\u003c\/p\u003e \u003cp\u003e4.9.2 Problems and Exercises 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Scale-space Vision 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Abstract 165\u003c\/p\u003e \u003cp\u003e5.2 Basic Concepts 165\u003c\/p\u003e \u003cp\u003e5.2.1 Context 165\u003c\/p\u003e \u003cp\u003e5.2.2 Image Scale 166\u003c\/p\u003e \u003cp\u003e5.2.3 Image Matching Over Scale 166\u003c\/p\u003e \u003cp\u003e5.3 Constructing a Scale-space 168\u003c\/p\u003e \u003cp\u003e5.3.1 Gaussian Scale-space 168\u003c\/p\u003e \u003cp\u003e5.3.2 Differential Scale-space 170\u003c\/p\u003e \u003cp\u003e5.4 Multi-resolution Pyramids 172\u003c\/p\u003e \u003cp\u003e5.4.1 Introducing Multi-resolution Pyramids 172\u003c\/p\u003e \u003cp\u003e5.4.2 How to Build Pyramids 175\u003c\/p\u003e \u003cp\u003e5.4.3 Constructing Regular Gaussian Pyramids 175\u003c\/p\u003e \u003cp\u003e5.4.4 Laplacian of Gaussian Pyramids 177\u003c\/p\u003e \u003cp\u003e5.4.5 Expanding Pyramid Levels 178\u003c\/p\u003e \u003cp\u003e5.4.6 Semi-pyramids 179\u003c\/p\u003e \u003cp\u003e5.5 Practical Examples 181\u003c\/p\u003e \u003cp\u003e5.5.1 C++ Examples 181\u003c\/p\u003e \u003cp\u003e5.5.2 Matlab Examples 186\u003c\/p\u003e \u003cp\u003e5.6 Closure 191\u003c\/p\u003e \u003cp\u003e5.6.1 Chapter Summary 191\u003c\/p\u003e \u003cp\u003e5.6.2 Further Reading 191\u003c\/p\u003e \u003cp\u003e5.6.3 Problems and Exercises 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Image Matching Algorithms 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Abstract 193\u003c\/p\u003e \u003cp\u003e6.2 Basic Concepts 193\u003c\/p\u003e \u003cp\u003e6.3 Match Measures 194\u003c\/p\u003e \u003cp\u003e6.3.1 Distances of Image Regions 194\u003c\/p\u003e \u003cp\u003e6.3.2 Matching Distances for Bit Strings 198\u003c\/p\u003e \u003cp\u003e6.3.3 Matching Distances for Multichannel Images 199\u003c\/p\u003e \u003cp\u003e6.3.4 Measures Based on Theory of Information 202\u003c\/p\u003e \u003cp\u003e6.3.5 Histogram Matching 205\u003c\/p\u003e \u003cp\u003e6.3.6 Efficient Computations of Distances 206\u003c\/p\u003e \u003cp\u003e6.3.7 Nonparametric Image Transformations 209\u003c\/p\u003e \u003cp\u003e6.3.8 Log-polar Transformation for Image Matching 218\u003c\/p\u003e \u003cp\u003e6.4 Computational Aspects of Matching 222\u003c\/p\u003e \u003cp\u003e6.4.1 Occlusions 222\u003c\/p\u003e \u003cp\u003e6.4.2 Disparity Estimation with Subpixel Accuracy 224\u003c\/p\u003e \u003cp\u003e6.4.3 Evaluation Methods for Stereo Algorithms 226\u003c\/p\u003e \u003cp\u003e6.5 Diversity of Stereo Matching Methods 229\u003c\/p\u003e \u003cp\u003e6.5.1 Structure of Stereo Matching Algorithms 233\u003c\/p\u003e \u003cp\u003e6.6 Area-based Matching 238\u003c\/p\u003e \u003cp\u003e6.6.1 Basic Search Approach 239\u003c\/p\u003e \u003cp\u003e6.6.2 Interpreting Match Cost 241\u003c\/p\u003e \u003cp\u003e6.6.3 Point-oriented Implementation 245\u003c\/p\u003e \u003cp\u003e6.6.4 Disparity-oriented Implementation 250\u003c\/p\u003e \u003cp\u003e6.6.5 Complexity of Area-based Matching 256\u003c\/p\u003e \u003cp\u003e6.6.6 Disparity Map Cross-checking 257\u003c\/p\u003e \u003cp\u003e6.6.7 Area-based Matching in Practice 259\u003c\/p\u003e \u003cp\u003e6.7 Area-based Elastic Matching 273\u003c\/p\u003e \u003cp\u003e6.7.1 Elastic Matching at a Single Scale 273\u003c\/p\u003e \u003cp\u003e6.7.2 Elastic Matching Concept 278\u003c\/p\u003e \u003cp\u003e6.7.3 Scale-based Search 280\u003c\/p\u003e \u003cp\u003e6.7.4 Coarse-to-fine Matching Over Scale 283\u003c\/p\u003e \u003cp\u003e6.7.5 Scale Subdivision 284\u003c\/p\u003e \u003cp\u003e6.7.6 Confidence Over Scale 285\u003c\/p\u003e \u003cp\u003e6.7.7 Final Multi-resolution Matcher 286\u003c\/p\u003e \u003cp\u003e6.8 Feature-based Image Matching 288\u003c\/p\u003e \u003cp\u003e6.8.1 Zero-crossing Matching 289\u003c\/p\u003e \u003cp\u003e6.8.2 Corner-based Matching 292\u003c\/p\u003e \u003cp\u003e6.8.3 Edge-based Matching: The Shirai Method 295\u003c\/p\u003e \u003cp\u003e6.9 Gradient-based Matching 296\u003c\/p\u003e \u003cp\u003e6.10 Method of Dynamic Programming 298\u003c\/p\u003e \u003cp\u003e6.10.1 Dynamic Programming Formulation of the Stereo Problem 301\u003c\/p\u003e \u003cp\u003e6.11 Graph Cut Approach 306\u003c\/p\u003e \u003cp\u003e6.11.1 Graph Cut Algorithm 306\u003c\/p\u003e \u003cp\u003e6.11.2 Stereo as a Voxel Labelling Problem 311\u003c\/p\u003e \u003cp\u003e6.11.3 Stereo as a Pixel Labelling Problem 312\u003c\/p\u003e \u003cp\u003e6.12 Optical Flow 314\u003c\/p\u003e \u003cp\u003e6.13 Practical Examples 318\u003c\/p\u003e \u003cp\u003e6.13.1 Stereo Matching Hierarchy in C++ 318\u003c\/p\u003e \u003cp\u003e6.13.2 Log-polar Transformation 319\u003c\/p\u003e \u003cp\u003e6.14 Closure 321\u003c\/p\u003e \u003cp\u003e6.14.1 Further Reading 321\u003c\/p\u003e \u003cp\u003e6.14.2 Problems and Exercises 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Space Reconstruction and Multiview Integration 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Abstract 323\u003c\/p\u003e \u003cp\u003e7.2 General 3D Reconstruction 323\u003c\/p\u003e \u003cp\u003e7.2.1 Triangulation 324\u003c\/p\u003e \u003cp\u003e7.2.2 Reconstruction up to a Scale 325\u003c\/p\u003e \u003cp\u003e7.2.3 Reconstruction up to a Projective Transformation 327\u003c\/p\u003e \u003cp\u003e7.3 Multiview Integration 329\u003c\/p\u003e \u003cp\u003e7.3.1 Implicit Surfaces and Marching Cubes 330\u003c\/p\u003e \u003cp\u003e7.3.2 Direct Mesh Integration 338\u003c\/p\u003e \u003cp\u003e7.4 Closure 342\u003c\/p\u003e \u003cp\u003e7.4.1 Further Reading 342\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Case Examples 343\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Abstract 343\u003c\/p\u003e \u003cp\u003e8.2 3D System for Vision-Impaired Persons 343\u003c\/p\u003e \u003cp\u003e8.3 Face and Body Modelling 345\u003c\/p\u003e \u003cp\u003e8.3.1 Development of Face and Body Capture Systems 345\u003c\/p\u003e \u003cp\u003e8.3.2 Imaging Resolution, 3D Resolution and Implications for Applications 346\u003c\/p\u003e \u003cp\u003e8.3.3 3D Capture and Analysis Pipeline for Constructing Virtual Humans 350\u003c\/p\u003e \u003cp\u003e8.4 Clinical and Veterinary Applications 352\u003c\/p\u003e \u003cp\u003e8.4.1 Development of 3D Clinical Photography 352\u003c\/p\u003e \u003cp\u003e8.4.2 Clinical Requirements for 3D Imaging 353\u003c\/p\u003e \u003cp\u003e8.4.3 Clinical Assessment Based on 3D Surface Anatomy 353\u003c\/p\u003e \u003cp\u003e8.4.4 Extraction of Basic 3D Anatomic Measurements 354\u003c\/p\u003e \u003cp\u003e8.4.5 Vector Field Surface Analysis by Means of Dense Correspondences 357\u003c\/p\u003e \u003cp\u003e8.4.6 Eigenspace Methods 359\u003c\/p\u003e \u003cp\u003e8.4.7 Clinical and Veterinary Examples 362\u003c\/p\u003e \u003cp\u003e8.4.8 Multimodal 3D Imaging 367\u003c\/p\u003e \u003cp\u003e8.5 Movie Restoration 370\u003c\/p\u003e \u003cp\u003e8.6 Closure 374\u003c\/p\u003e \u003cp\u003e8.6.1 Further Reading 374\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Basics of the Projective Geometry 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Abstract 377\u003c\/p\u003e \u003cp\u003e9.2 Homogeneous Coordinates 377\u003c\/p\u003e \u003cp\u003e9.3 Point, Line and the Rule of Duality 379\u003c\/p\u003e \u003cp\u003e9.4 Point and Line at Infinity 380\u003c\/p\u003e \u003cp\u003e9.5 Basics on Conics 382\u003c\/p\u003e \u003cp\u003e9.5.1 Conics in ℘2 382\u003c\/p\u003e \u003cp\u003e9.5.2 Conics in ℘2 384\u003c\/p\u003e \u003cp\u003e9.6 Group of Projective Transformations 385\u003c\/p\u003e \u003cp\u003e9.6.1 Projective Base 385\u003c\/p\u003e \u003cp\u003e9.6.2 Hyperplanes 386\u003c\/p\u003e \u003cp\u003e9.6.3 Projective Homographies 386\u003c\/p\u003e \u003cp\u003e9.7 Projective Invariants 387\u003c\/p\u003e \u003cp\u003e9.8 Closure 388\u003c\/p\u003e \u003cp\u003e9.8.1 Further Reading 389\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Basics of Tensor Calculus for Image Processing 391\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Abstract 391\u003c\/p\u003e \u003cp\u003e10.2 Basic Concepts 391\u003c\/p\u003e \u003cp\u003e10.2.1 Linear Operators 392\u003c\/p\u003e \u003cp\u003e10.2.2 Change of Coordinate Systems: Jacobians 393\u003c\/p\u003e \u003cp\u003e10.3 Change of a Base 394\u003c\/p\u003e \u003cp\u003e10.4 Laws of Tensor Transformations 396\u003c\/p\u003e \u003cp\u003e10.5 The Metric Tensor 397\u003c\/p\u003e \u003cp\u003e10.5.1 Covariant and Contravariant Components in a Curvilinear Coordinate System 397\u003c\/p\u003e \u003cp\u003e10.5.2 The First Fundamental Form 399\u003c\/p\u003e \u003cp\u003e10.6 Simple Tensor Algebra 399\u003c\/p\u003e \u003cp\u003e10.6.1 Tensor Summation 399\u003c\/p\u003e \u003cp\u003e10.6.2 Tensor Product 400\u003c\/p\u003e \u003cp\u003e10.6.3 Contraction and Tensor Inner Product 400\u003c\/p\u003e \u003cp\u003e10.6.4 Reduction to Principal Axes 400\u003c\/p\u003e \u003cp\u003e10.6.5 Tensor Invariants 401\u003c\/p\u003e \u003cp\u003e10.7 Closure 401\u003c\/p\u003e \u003cp\u003e10.7.1 Further Reading 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Distortions and Noise in Images 403\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Abstract 403\u003c\/p\u003e \u003cp\u003e11.2 Types and Models of Noise 403\u003c\/p\u003e \u003cp\u003e11.3 Generating Noisy Test Images 405\u003c\/p\u003e \u003cp\u003e11.4 Generating Random Numbers with Normal Distributions 407\u003c\/p\u003e \u003cp\u003e11.5 Closure 408\u003c\/p\u003e \u003cp\u003e11.5.1 Further Reading 408\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Image Warping Procedures 409\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Abstract 409\u003c\/p\u003e \u003cp\u003e12.2 Architecture of the Warping System 409\u003c\/p\u003e \u003cp\u003e12.3 Coordinate Transformation Module 410\u003c\/p\u003e \u003cp\u003e12.3.1 Projective and Affine Transformations of a Plane 410\u003c\/p\u003e \u003cp\u003e12.3.2 Polynomial Transformations 411\u003c\/p\u003e \u003cp\u003e12.3.3 Generic Coordinates Mapping 412\u003c\/p\u003e \u003cp\u003e12.4 Interpolation of Pixel Values 412\u003c\/p\u003e \u003cp\u003e12.4.1 Bilinear Interpolation 412\u003c\/p\u003e \u003cp\u003e12.4.2 Interpolation of Nonscalar-Valued Pixels 414\u003c\/p\u003e \u003cp\u003e12.5 The Warp Engine 414\u003c\/p\u003e \u003cp\u003e12.6 Software Model of the Warping Schemes 415\u003c\/p\u003e \u003cp\u003e12.6.1 Coordinate Transformation Hierarchy 415\u003c\/p\u003e \u003cp\u003e12.6.2 Interpolation Hierarchy 416\u003c\/p\u003e \u003cp\u003e12.6.3 Image Warp Hierarchy 416\u003c\/p\u003e \u003cp\u003e12.7 Warp Examples 419\u003c\/p\u003e \u003cp\u003e12.8 Finding the Linear Transformation from Point Correspondences 420\u003c\/p\u003e \u003cp\u003e12.8.1 Linear Algebra on Images 424\u003c\/p\u003e \u003cp\u003e12.9 Closure 427\u003c\/p\u003e \u003cp\u003e12.9.1 Further Reading 428\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Programming Techniques for Image Processing and Computer Vision 429\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Abstract 429\u003c\/p\u003e \u003cp\u003e13.2 Useful Techniques and Methodology 430\u003c\/p\u003e \u003cp\u003e13.2.1 Design and Implementation 430\u003c\/p\u003e \u003cp\u003e13.2.2 Template Classes 436\u003c\/p\u003e \u003cp\u003e13.2.3 Asserting Code Correctness 438\u003c\/p\u003e \u003cp\u003e13.2.4 Debugging Issues 440\u003c\/p\u003e \u003cp\u003e13.3 Design Patterns 441\u003c\/p\u003e \u003cp\u003e13.3.1 Template Function Objects 441\u003c\/p\u003e \u003cp\u003e13.3.2 Handle-body or Bridge 442\u003c\/p\u003e \u003cp\u003e13.3.3 Composite 445\u003c\/p\u003e \u003cp\u003e13.3.4 Strategy 447\u003c\/p\u003e \u003cp\u003e13.3.5 Class Policies and Traits 448\u003c\/p\u003e \u003cp\u003e13.3.6 Singleton 450\u003c\/p\u003e \u003cp\u003e13.3.7 Proxy 450\u003c\/p\u003e \u003cp\u003e13.3.8 Factory Method 451\u003c\/p\u003e \u003cp\u003e13.3.9 Prototype 452\u003c\/p\u003e \u003cp\u003e13.4 Object Lifetime and Memory Management 453\u003c\/p\u003e \u003cp\u003e13.5 Image Processing Platforms 455\u003c\/p\u003e \u003cp\u003e13.5.1 Image Processing Libraries 455\u003c\/p\u003e \u003cp\u003e13.5.2 Writing Software for Different Platforms 455\u003c\/p\u003e \u003cp\u003e13.6 Closure 456\u003c\/p\u003e \u003cp\u003e13.6.1 Further Reading 456\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Image Processing Library 457\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 459\u003c\/p\u003e \u003cp\u003eIndex 475\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402255933783,"sku":"9780470017043","price":99.86,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470017043.jpg?v=1730479853"},{"product_id":"image-processing-27-adaptive-and-cognitive-dynamic-systems-signal-processing-learning-communications-and-control-9780471406372","title":"Image Processing 27 Adaptive and Cognitive","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIntelligent Image Processing describes the EyeTap technology that allows non-invasive tapping into the human eye through devices built into eyeglass frames. This isn't merely about a computer screen inside eyeglasses, but rather the ability to have a shared telepathic experience among viewers.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface\u003cbr\u003e \u003cbr\u003e 1 Humanistic Intelligence as a Basis for Intelligent Image Processing\u003cbr\u003e \u003cbr\u003e 1.1 Humanistic Intelligence\/\u003cbr\u003e \u003cbr\u003e 1.2 \"WearComp\" as Means of Realizing Humanistic Intelligence\u003cbr\u003e \u003cbr\u003e 1.3 Practical Embodiments of Humanistic Intelligence\u003cbr\u003e \u003cbr\u003e 2 Where on the Body is the Best Place for a Personal Imaging System?\u003cbr\u003e \u003cbr\u003e 2.1 Portable Imaging Systems\u003cbr\u003e \u003cbr\u003e 2.2 Personal Handheld Systems\u003cbr\u003e \u003cbr\u003e 2.3 Concomitant Cover Activities and the Videoclips Camera System\u003cbr\u003e \u003cbr\u003e 2.4 The Wristwatch Videophone: A Fully Functional \"Always Ready\" Prototype\u003cbr\u003e \u003cbr\u003e 2.5 Telepointer: Wearable Hands-Free Completely Self-Contained Visual Augmented Reality\u003cbr\u003e \u003cbr\u003e 2.6 Portable Personal Pulse Doppler Radar Vision System Based on Time-Frequency Analysis and q-Chirplet Transform\u003cbr\u003e \u003cbr\u003e 2.7 When Both Camera and Display are Headworn: Personal Imaging and Mediated Reality\u003cbr\u003e \u003cbr\u003e 2.8 Partially Mediated Reality\u003cbr\u003e \u003cbr\u003e 2.9 Seeing \"Eye-to-Eye\"\u003cbr\u003e \u003cbr\u003e 2.10 Exercises, Problem Sets, and Homework\u003cbr\u003e \u003cbr\u003e 3 The EyeTap Principle: Effectively Locating the Camera Inside the Eye as an Alternative to Wearable Camera Systems\u003cbr\u003e \u003cbr\u003e 3.1 A Personal Imaging System for Lifelong Video Capture\u003cbr\u003e \u003cbr\u003e 3.2 The EyeTap Principle\u003cbr\u003e \u003cbr\u003e 3.3 Practical Embodiments of EyeTap\u003cbr\u003e \u003cbr\u003e 3.4 Problems with Previously Known Camera Viewfinders\u003cbr\u003e \u003cbr\u003e 3.5 The Aremac\u003cbr\u003e \u003cbr\u003e 3.6 The Foveated Personal Imaging System\u003cbr\u003e \u003cbr\u003e 3.7 Teaching the EyeTap Principle\u003cbr\u003e \u003cbr\u003e 3.8 Calibration of EyeTap Systems\u003cbr\u003e \u003cbr\u003e 3.9 Using the Device as a Reality Mediator\u003cbr\u003e \u003cbr\u003e 3.10 User Studies\u003cbr\u003e \u003cbr\u003e 3.11 Summary and Conclusions\u003cbr\u003e \u003cbr\u003e 3.12 Exercises, Problem Sets, and Homework\u003cbr\u003e \u003cbr\u003e 4 Comparametric Equations, Quantigraphic Image Processing, and Comparagraphic Rendering\u003cbr\u003e \u003cbr\u003e 4.1 Historical Background\u003cbr\u003e \u003cbr\u003e 4.2 The Wyckoff Principle and the Range of Light\u003cbr\u003e \u003cbr\u003e 4.3 Comparametric Image Processing: Comparing Differently Exposed Images of the Same Subject Matter\u003cbr\u003e \u003cbr\u003e 4.4 The Comparagram: Practical Implementations of Comparanalysis\u003cbr\u003e \u003cbr\u003e 4.5 Spatiotonal Photoquantigraphic Filters\u003cbr\u003e \u003cbr\u003e 4.6 Glossary of Functions\u003cbr\u003e \u003cbr\u003e 4.7 Exercises, Problem Sets, and Homework\u003cbr\u003e \u003cbr\u003e 5 Lightspace and Antihomomorphic Vector Spaces\u003cbr\u003e \u003cbr\u003e 5.1 Lightspace\u003cbr\u003e \u003cbr\u003e 5.2 The Lightspace Analysis Function\u003cbr\u003e \u003cbr\u003e 5.3 The \"Spotflash\" Primitive\u003cbr\u003e \u003cbr\u003e 5.4 LAF×LSF Imaging (\"Lightspace\")\u003cbr\u003e \u003cbr\u003e 5.5 Lightspace Subspaces\u003cbr\u003e \u003cbr\u003e 5.6 \"Lightvector\" Subspace\u003cbr\u003e \u003cbr\u003e 5.7 Painting with Lightvectors: Photographic\/Videographic Origins and Applications of WearComp-Based Mediated Reality\u003cbr\u003e \u003cbr\u003e 5.8 Collaborative Mediated Reality Field Trials\u003cbr\u003e \u003cbr\u003e 5.9 Conclusions\u003cbr\u003e \u003cbr\u003e 5.10 Exercises, Problem Sets, and Homework\u003cbr\u003e \u003cbr\u003e 6 VideoOrbits: The Projective Geometry Renaissance\u003cbr\u003e \u003cbr\u003e 6.1 VideoOrbits\u003cbr\u003e \u003cbr\u003e 6.2 Background\u003cbr\u003e \u003cbr\u003e 6.3 Framework: Motion Parameter Estimation and Optical Flow\u003cbr\u003e \u003cbr\u003e 6.4 Multiscale Implementations in 2-D\u003cbr\u003e \u003cbr\u003e 6.5 Performance and Applications\u003cbr\u003e \u003cbr\u003e 6.6 AGC and the Range of Light\u003cbr\u003e \u003cbr\u003e 6.7 Joint Estimation of Both Domain and Range Coordinate Transformations\u003cbr\u003e \u003cbr\u003e 6.8 The Big Picture\u003cbr\u003e \u003cbr\u003e 6.9 Reality Window Manager\u003cbr\u003e \u003cbr\u003e 6.10 Application of Orbits: The Photonic Firewall\u003cbr\u003e \u003cbr\u003e 6.11 All the World's a Skinner Box\u003cbr\u003e \u003cbr\u003e 6.12 Blocking Spam with a Photonic Filter\u003cbr\u003e \u003cbr\u003e 6.13 Exercises, Problem Sets, and Homework\u003cbr\u003e \u003cbr\u003e Appendix A: Safety First!\u003cbr\u003e \u003cbr\u003e Appendix B: Multiambic Keyer for Use While Engaged in Other Activities\u003cbr\u003e \u003cbr\u003e B.1 Introduction\u003cbr\u003e \u003cbr\u003e B.2 Background and Terminology on Keyers\u003cbr\u003e \u003cbr\u003e B.3 Optimal Keyer Design: The Conformal Keyer\u003cbr\u003e \u003cbr\u003e B.4 The Seven Stages of a Keypress\u003cbr\u003e \u003cbr\u003e B.5 The Pentakeyer\u003cbr\u003e \u003cbr\u003e B.6 Redundancy\u003cbr\u003e \u003cbr\u003e B.7 Ordinally Conditional Modifiers\u003cbr\u003e \u003cbr\u003e B.8 Rollover\u003cbr\u003e \u003cbr\u003e B.8.1 Example of Rollover on a Cybernetic Keyer\u003cbr\u003e \u003cbr\u003e B.9 Further Increasing the Chordic Redundancy Factor: A More Expressive Keyer\u003cbr\u003e \u003cbr\u003e B.10 Including One Time Constant\u003cbr\u003e \u003cbr\u003e B.11 Making a Conformal Multiambic Keyer\u003cbr\u003e \u003cbr\u003e B.12 Comparison to Related Work\u003cbr\u003e \u003cbr\u003e B.13 Conclusion\u003cbr\u003e \u003cbr\u003e B.14 Acknowledgments\u003cbr\u003e \u003cbr\u003e Appendix C: WearCam GNUX Howto\u003cbr\u003e \u003cbr\u003e C.1 Installing GNUX on WearComps\u003cbr\u003e \u003cbr\u003e C.2 Getting Started\u003cbr\u003e \u003cbr\u003e C.3 Stop the Virus from Running\u003cbr\u003e \u003cbr\u003e C.4 Making Room for an Operating System\u003cbr\u003e \u003cbr\u003e C.5 Other Needed Files\u003cbr\u003e \u003cbr\u003e C.6 Defrag \/\u003cbr\u003e 323\u003cbr\u003e \u003cbr\u003e C.7 Fips\u003cbr\u003e \u003cbr\u003e C.8 Starting Up in GNUX with Ramdisk\u003cbr\u003e \u003cbr\u003e Appendix D: How to Build a Covert Computer Imaging System into Ordinary Looking Sunglasses\u003cbr\u003e \u003cbr\u003e D.1 The Move from Sixth-Generation WearComp to Seventh-Generation\u003cbr\u003e \u003cbr\u003e D.2 Label the Wires!\u003cbr\u003e \u003cbr\u003e D.3 Soldering Wires Directly to the Kopin CyberDisplay\u003cbr\u003e \u003cbr\u003e D.4 Completing the Computershades\u003cbr\u003e \u003cbr\u003e Bibliography\u003cbr\u003e \u003cbr\u003e Index","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402589249879,"sku":"9780471406372","price":127.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471406372.jpg?v=1730480869"},{"product_id":"understanding-vision-9780631179092","title":"Understanding Vision","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIn recent years there have been major advances in understanding visual processing. This work brings together experts from various disciplines, ranging from computer science to neuropsychology, to discuss how the work carried out in their field fits into the broader context of vision research.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eContructing the perception of surfaces from multiple cues, Kent A. Stevens' visual analysis and representation of spatial relations, Roger J. Watt; modern theories of Gestalt perception, Stephen J. Palmer; thinking visually, Kris N. Kirby and Stephen M. Kosslyn; perceiving and recognizing faces, Vicki Bruce; the breakdown approach to visual perception - neuropsychological studies of object recognition, Glyn W. Humphreys et al; mechanisms which mediate discrimination of 2-D spatial patterns in distributed images, Keith H. Ruddock; the analysis of 3-D shape - psychological principles and neural mechanisms, Andrew J. Parker et al; identification of disoriented objects - a dual-systems theory, Pierre Jolicoeur; surface layout from retinal flow, Mike Harris et al; neural facades - visual representations of static and moving form-and-colour-and-depth, Stephen Grossberg.","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":49403393573207,"sku":"9780631179092","price":37.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780631179092.jpg?v=1730483330"},{"product_id":"ai-at-the-edge-9781098120207","title":"AI at the Edge","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406792368471,"sku":"9781098120207","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098120207.jpg?v=1730497127"},{"product_id":"a-practical-introduction-to-computer-vision-with-opencv-9781118848456","title":"A Practical Introduction to Computer Vision with","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eExplains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eComputer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers \u0026amp; tablets\/ cameras built into our mobile phones\/ cameras in games \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e“Although there are many computer vision books on the market that offer a more comprehensive approach to explaining the computer vision concepts, extremely few offer such comprehensive practical examples. In this context, the book would be very welcome by beginner code developers.\"  (\u003ci\u003eComputing Reviews\u003c\/i\u003e, 8 August 2014)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface xiii  \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 A Difficult Problem 1\u003c\/p\u003e \u003cp\u003e1.2 The Human Vision System 2\u003c\/p\u003e \u003cp\u003e1.3 Practical Applications of Computer Vision 3\u003c\/p\u003e \u003cp\u003e1.4 The Future of Computer Vision 5\u003c\/p\u003e \u003cp\u003e1.5 Material in This Textbook 6\u003c\/p\u003e \u003cp\u003e1.6 Going Further with Computer Vision 7\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Images 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Cameras 9\u003c\/p\u003e \u003cp\u003e2.1.1 The Simple Pinhole Camera Model 9\u003c\/p\u003e \u003cp\u003e2.2 Images 10\u003c\/p\u003e \u003cp\u003e2.2.1 Sampling 11\u003c\/p\u003e \u003cp\u003e2.2.2 Quantisation 11\u003c\/p\u003e \u003cp\u003e2.3 Colour Images 13\u003c\/p\u003e \u003cp\u003e2.3.1 Red–Green–Blue (RGB) Images 14\u003c\/p\u003e \u003cp\u003e2.3.2 Cyan–Magenta–Yellow (CMY) Images 17\u003c\/p\u003e \u003cp\u003e2.3.3 YUV Images 17\u003c\/p\u003e \u003cp\u003e2.3.4 Hue Luminance Saturation (HLS) Images 18\u003c\/p\u003e \u003cp\u003e2.3.5 Other Colour Spaces 20\u003c\/p\u003e \u003cp\u003e2.3.6 Some Colour Applications 20\u003c\/p\u003e \u003cp\u003e2.4 Noise 22\u003c\/p\u003e \u003cp\u003e2.4.1 Types of Noise 23\u003c\/p\u003e \u003cp\u003e2.4.2 Noise Models 25\u003c\/p\u003e \u003cp\u003e2.4.3 Noise Generation 26\u003c\/p\u003e \u003cp\u003e2.4.4 Noise Evaluation 26\u003c\/p\u003e \u003cp\u003e2.5 Smoothing 27\u003c\/p\u003e \u003cp\u003e2.5.1 Image Averaging 27\u003c\/p\u003e \u003cp\u003e2.5.2 Local Averaging and Gaussian Smoothing 28\u003c\/p\u003e \u003cp\u003e2.5.3 Rotating Mask 30\u003c\/p\u003e \u003cp\u003e2.5.4 Median Filter 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Histograms 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 1D Histograms 35\u003c\/p\u003e \u003cp\u003e3.1.1 Histogram Smoothing 36\u003c\/p\u003e \u003cp\u003e3.1.2 Colour Histograms 37\u003c\/p\u003e \u003cp\u003e3.2 3D Histograms 39\u003c\/p\u003e \u003cp\u003e3.3 Histogram\/Image Equalisation 40\u003c\/p\u003e \u003cp\u003e3.4 Histogram Comparison 41\u003c\/p\u003e \u003cp\u003e3.5 Back-projection 43\u003c\/p\u003e \u003cp\u003e3.6 k-means Clustering 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Binary Vision 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Thresholding 49\u003c\/p\u003e \u003cp\u003e4.1.1 Thresholding Problems 50\u003c\/p\u003e \u003cp\u003e4.2 Threshold Detection Methods 51\u003c\/p\u003e \u003cp\u003e4.2.1 Bimodal Histogram Analysis 52\u003c\/p\u003e \u003cp\u003e4.2.2 Optimal Thresholding 52\u003c\/p\u003e \u003cp\u003e4.2.3 Otsu Thresholding 54\u003c\/p\u003e \u003cp\u003e4.3 Variations on Thresholding 56\u003c\/p\u003e \u003cp\u003e4.3.1 Adaptive Thresholding 56\u003c\/p\u003e \u003cp\u003e4.3.2 Band Thresholding 57\u003c\/p\u003e \u003cp\u003e4.3.3 Semi-thresholding 58\u003c\/p\u003e \u003cp\u003e4.3.4 Multispectral Thresholding 58\u003c\/p\u003e \u003cp\u003e4.4 Mathematical Morphology 59\u003c\/p\u003e \u003cp\u003e4.4.1 Dilation 60\u003c\/p\u003e \u003cp\u003e4.4.2 Erosion 62\u003c\/p\u003e \u003cp\u003e4.4.3 Opening and Closing 63\u003c\/p\u003e \u003cp\u003e4.4.4 Grey-scale and Colour Morphology 65\u003c\/p\u003e \u003cp\u003e4.5 Connectivity 66\u003c\/p\u003e \u003cp\u003e4.5.1 Connectedness: Paradoxes and Solutions 66\u003c\/p\u003e \u003cp\u003e4.5.2 Connected Components Analysis 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Geometric Transformations 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Problem Specification and Algorithm 71\u003c\/p\u003e \u003cp\u003e5.2 Affine Transformations 73\u003c\/p\u003e \u003cp\u003e5.2.1 Known Affine Transformations 74\u003c\/p\u003e \u003cp\u003e5.2.2 Unknown Affine Transformations 75\u003c\/p\u003e \u003cp\u003e5.3 Perspective Transformations 76\u003c\/p\u003e \u003cp\u003e5.4 Specification of More Complex Transformations 78\u003c\/p\u003e \u003cp\u003e5.5 Interpolation 78\u003c\/p\u003e \u003cp\u003e5.5.1 Nearest Neighbour Interpolation 79\u003c\/p\u003e \u003cp\u003e5.5.2 Bilinear Interpolation 79\u003c\/p\u003e \u003cp\u003e5.5.3 Bi-Cubic Interpolation 80\u003c\/p\u003e \u003cp\u003e5.6 Modelling and Removing Distortion from Cameras 80\u003c\/p\u003e \u003cp\u003e5.6.1 Camera Distortions 81\u003c\/p\u003e \u003cp\u003e5.6.2 Camera Calibration and Removing Distortion 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Edges 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Edge Detection 83\u003c\/p\u003e \u003cp\u003e6.1.1 First Derivative Edge Detectors 85\u003c\/p\u003e \u003cp\u003e6.1.2 Second Derivative Edge Detectors 92\u003c\/p\u003e \u003cp\u003e6.1.3 Multispectral Edge Detection 97\u003c\/p\u003e \u003cp\u003e6.1.4 Image Sharpening 98\u003c\/p\u003e \u003cp\u003e6.2 Contour Segmentation 99\u003c\/p\u003e \u003cp\u003e6.2.1 Basic Representations of Edge Data 99\u003c\/p\u003e \u003cp\u003e6.2.2 Border Detection 102\u003c\/p\u003e \u003cp\u003e6.2.3 Extracting Line Segment Representations of Edge Contours 105\u003c\/p\u003e \u003cp\u003e6.3 Hough Transform 108\u003c\/p\u003e \u003cp\u003e6.3.1 Hough for Lines 109\u003c\/p\u003e \u003cp\u003e6.3.2 Hough for Circles 111\u003c\/p\u003e \u003cp\u003e6.3.3 Generalised Hough 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Features 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Moravec Corner Detection 117\u003c\/p\u003e \u003cp\u003e7.2 Harris Corner Detection 118\u003c\/p\u003e \u003cp\u003e7.3 FAST Corner Detection 121\u003c\/p\u003e \u003cp\u003e7.4 SIFT 122\u003c\/p\u003e \u003cp\u003e7.4.1 Scale Space Extrema Detection 123\u003c\/p\u003e \u003cp\u003e7.4.2 Accurate Keypoint Location 124\u003c\/p\u003e \u003cp\u003e7.4.3 Keypoint Orientation Assignment 126\u003c\/p\u003e \u003cp\u003e7.4.4 Keypoint Descriptor 127\u003c\/p\u003e \u003cp\u003e7.4.5 Matching Keypoints 127\u003c\/p\u003e \u003cp\u003e7.4.6 Recognition 127\u003c\/p\u003e \u003cp\u003e7.5 Other Detectors 129\u003c\/p\u003e \u003cp\u003e7.5.1 Minimum Eigenvalues 130\u003c\/p\u003e \u003cp\u003e7.5.2 SURF 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Recognition 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Template Matching 131\u003c\/p\u003e \u003cp\u003e8.1.1 Applications 131\u003c\/p\u003e \u003cp\u003e8.1.2 Template Matching Algorithm 133\u003c\/p\u003e \u003cp\u003e8.1.3 Matching Metrics 134\u003c\/p\u003e \u003cp\u003e8.1.4 Finding Local Maxima or Minima 135\u003c\/p\u003e \u003cp\u003e8.1.5 Control Strategies for Matching 137\u003c\/p\u003e \u003cp\u003e8.2 Chamfer Matching 137\u003c\/p\u003e \u003cp\u003e8.2.1 Chamfering Algorithm 137\u003c\/p\u003e \u003cp\u003e8.2.2 Chamfer Matching Algorithm 139\u003c\/p\u003e \u003cp\u003e8.3 Statistical Pattern Recognition 140\u003c\/p\u003e \u003cp\u003e8.3.1 Probability Review 142\u003c\/p\u003e \u003cp\u003e8.3.2 Sample Features 143\u003c\/p\u003e \u003cp\u003e8.3.3 Statistical Pattern Recognition Technique 149\u003c\/p\u003e \u003cp\u003e8.4 Cascade of Haar Classifiers 152\u003c\/p\u003e \u003cp\u003e8.4.1 Features 154\u003c\/p\u003e \u003cp\u003e8.4.2 Training 156\u003c\/p\u003e \u003cp\u003e8.4.3 Classifiers 156\u003c\/p\u003e \u003cp\u003e8.4.4 Recognition 158\u003c\/p\u003e \u003cp\u003e8.5 Other Recognition Techniques 158\u003c\/p\u003e \u003cp\u003e8.5.1 Support Vector Machines (SVM) 158\u003c\/p\u003e \u003cp\u003e8.5.2 Histogram of Oriented Gradients (HoG) 159\u003c\/p\u003e \u003cp\u003e8.6 Performance 160\u003c\/p\u003e \u003cp\u003e8.6.1 Image and Video Datasets 160\u003c\/p\u003e \u003cp\u003e8.6.2 Ground Truth 161\u003c\/p\u003e \u003cp\u003e8.6.3 Metrics for Assessing Classification Performance 162\u003c\/p\u003e \u003cp\u003e8.6.4 Improving Computation Time 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Video 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Moving Object Detection 167\u003c\/p\u003e \u003cp\u003e9.1.1 Object of Interest 168\u003c\/p\u003e \u003cp\u003e9.1.2 Common Problems 168\u003c\/p\u003e \u003cp\u003e9.1.3 Difference Images 169\u003c\/p\u003e \u003cp\u003e9.1.4 Background Models 171\u003c\/p\u003e \u003cp\u003e9.1.5 Shadow Detection 179\u003c\/p\u003e \u003cp\u003e9.2 Tracking 180\u003c\/p\u003e \u003cp\u003e9.2.1 Exhaustive Search 181\u003c\/p\u003e \u003cp\u003e9.2.2 Mean Shift 181\u003c\/p\u003e \u003cp\u003e9.2.3 Dense Optical Flow 182\u003c\/p\u003e \u003cp\u003e9.2.4 Feature Based Optical Flow 185\u003c\/p\u003e \u003cp\u003e9.3 Performance 186\u003c\/p\u003e \u003cp\u003e9.3.1 Video Datasets (and Formats) 186\u003c\/p\u003e \u003cp\u003e9.3.2 Metrics for Assessing Video Tracking Performance 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Vision Problems 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Baby Food 189\u003c\/p\u003e \u003cp\u003e10.2 Labels on Glue 190\u003c\/p\u003e \u003cp\u003e10.3 O-rings 191\u003c\/p\u003e \u003cp\u003e10.4 Staying in Lane 192\u003c\/p\u003e \u003cp\u003e10.5 Reading Notices 193\u003c\/p\u003e \u003cp\u003e10.6 Mailboxes 194\u003c\/p\u003e \u003cp\u003e10.7 Abandoned and Removed Object Detection 195\u003c\/p\u003e \u003cp\u003e10.8 Surveillance 196\u003c\/p\u003e \u003cp\u003e10.9 Traffic Lights 197\u003c\/p\u003e \u003cp\u003e10.10 Real Time Face Tracking 198\u003c\/p\u003e \u003cp\u003e10.11 Playing Pool 199\u003c\/p\u003e \u003cp\u003e10.12 Open Windows 200\u003c\/p\u003e \u003cp\u003e10.13 Modelling Doors 201\u003c\/p\u003e \u003cp\u003e10.14 Determining the Time from Analogue Clocks 202\u003c\/p\u003e \u003cp\u003e10.15 Which Page 203\u003c\/p\u003e \u003cp\u003e10.16 Nut\/Bolt\/Washer Classification 204\u003c\/p\u003e \u003cp\u003e10.17 Road Sign Recognition 205\u003c\/p\u003e \u003cp\u003e10.18 License Plates 206\u003c\/p\u003e \u003cp\u003e10.19 Counting Bicycles 207\u003c\/p\u003e \u003cp\u003e10.20 Recognise Paintings 208\u003c\/p\u003e \u003cp\u003eReferences 209\u003c\/p\u003e \u003cp\u003eIndex 213\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406934253911,"sku":"9781118848456","price":44.6,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118848456.jpg?v=1730497607"},{"product_id":"computer-vision-in-vehicle-technology-9781118868072","title":"Computer Vision in Vehicle Technology","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eComputer Vision in Vehicle Technology: Land, Sea \u0026amp; Air         Antonio M. Lopez, Universitat Autonoma de Barcelona, Spain    Atsushi Imiya, Chiba University, Japan    Tomas Pajdla, Czech Technical University, Prague    Jose M.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Contributors ix\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAbbreviations and Acronyms xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Computer Vision in Vehicles 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eReinhard Klette\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Adaptive Computer Vision for Vehicles 1\u003c\/p\u003e \u003cp\u003e1.1.1 Applications 1\u003c\/p\u003e \u003cp\u003e1.1.2 Traffic Safety and Comfort 2\u003c\/p\u003e \u003cp\u003e1.1.3 Strengths of (Computer) Vision 2\u003c\/p\u003e \u003cp\u003e1.1.4 Generic and Specific Tasks 3\u003c\/p\u003e \u003cp\u003e1.1.5 Multi-module Solutions 4\u003c\/p\u003e \u003cp\u003e1.1.6 Accuracy, Precision, and Robustness 5\u003c\/p\u003e \u003cp\u003e1.1.7 Comparative Performance Evaluation 5\u003c\/p\u003e \u003cp\u003e1.1.8 There Are Many Winners 6\u003c\/p\u003e \u003cp\u003e1.2 Notation and Basic Definitions 6\u003c\/p\u003e \u003cp\u003e1.2.1 Images and Videos 6\u003c\/p\u003e \u003cp\u003e1.2.2 Cameras 8\u003c\/p\u003e \u003cp\u003e1.2.3 Optimization 10\u003c\/p\u003e \u003cp\u003e1.3 Visual Tasks 12\u003c\/p\u003e \u003cp\u003e1.3.1 Distance 12\u003c\/p\u003e \u003cp\u003e1.3.2 Motion 16\u003c\/p\u003e \u003cp\u003e1.3.3 Object Detection and Tracking 18\u003c\/p\u003e \u003cp\u003e1.3.4 Semantic Segmentation 21\u003c\/p\u003e \u003cp\u003e1.4 Concluding Remarks 23\u003c\/p\u003e \u003cp\u003eAcknowledgments 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Autonomous Driving 24\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eUwe Franke\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 24\u003c\/p\u003e \u003cp\u003e2.1.1 The Dream 24\u003c\/p\u003e \u003cp\u003e2.1.2 Applications 25\u003c\/p\u003e \u003cp\u003e2.1.3 Level of Automation 26\u003c\/p\u003e \u003cp\u003e2.1.4 Important Research Projects 27\u003c\/p\u003e \u003cp\u003e2.1.5 Outdoor Vision Challenges 30\u003c\/p\u003e \u003cp\u003e2.2 Autonomous Driving in Cities 31\u003c\/p\u003e \u003cp\u003e2.2.1 Localization 33\u003c\/p\u003e \u003cp\u003e2.2.2 Stereo Vision-Based Perception in 3D 36\u003c\/p\u003e \u003cp\u003e2.2.3 Object Recognition 43\u003c\/p\u003e \u003cp\u003e2.3 Challenges 49\u003c\/p\u003e \u003cp\u003e2.3.1 Increasing Robustness 49\u003c\/p\u003e \u003cp\u003e2.3.2 Scene Labeling 50\u003c\/p\u003e \u003cp\u003e2.3.3 Intention Recognition 52\u003c\/p\u003e \u003cp\u003e2.4 Summary 52\u003c\/p\u003e \u003cp\u003eAcknowledgments 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Computer Vision for MAVs 55\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eFriedrich Fraundorfer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 55\u003c\/p\u003e \u003cp\u003e3.2 System and Sensors 57\u003c\/p\u003e \u003cp\u003e3.3 Ego-Motion Estimation 58\u003c\/p\u003e \u003cp\u003e3.3.1 State Estimation Using Inertial and Vision Measurements 58\u003c\/p\u003e \u003cp\u003e3.3.2 MAV Pose from Monocular Vision 62\u003c\/p\u003e \u003cp\u003e3.3.3 MAV Pose from Stereo Vision 63\u003c\/p\u003e \u003cp\u003e3.3.4 MAV Pose from Optical Flow Measurements 65\u003c\/p\u003e \u003cp\u003e3.4 3D Mapping 67\u003c\/p\u003e \u003cp\u003e3.5 Autonomous Navigation 71\u003c\/p\u003e \u003cp\u003e3.6 Scene Interpretation 72\u003c\/p\u003e \u003cp\u003e3.7 Concluding Remarks 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Exploring the Seafloor with Underwater Robots 75\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRafael Garcia, Nuno Gracias, Tudor Nicosevici, Ricard Prados, Natalia Hurtos, Ricard Campos, Javier Escartin, Armagan Elibol, Ramon Hegedus and Laszlo Neumann\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 75\u003c\/p\u003e \u003cp\u003e4.2 Challenges of Underwater Imaging 77\u003c\/p\u003e \u003cp\u003e4.3 Online Computer Vision Techniques 79\u003c\/p\u003e \u003cp\u003e4.3.1 Dehazing 79\u003c\/p\u003e \u003cp\u003e4.3.2 Visual Odometry 84\u003c\/p\u003e \u003cp\u003e4.3.3 SLAM 87\u003c\/p\u003e \u003cp\u003e4.3.4 Laser Scanning 91\u003c\/p\u003e \u003cp\u003e4.4 Acoustic Imaging Techniques 92\u003c\/p\u003e \u003cp\u003e4.4.1 Image Formation 92\u003c\/p\u003e \u003cp\u003e4.4.2 Online Techniques for Acoustic Processing 95\u003c\/p\u003e \u003cp\u003e4.5 Concluding Remarks 98\u003c\/p\u003e \u003cp\u003eAcknowledgments 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Vision-Based Advanced Driver Assistance Systems 100\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDavid Gerónimo, David Vázquez and Arturo de la Escalera\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 100\u003c\/p\u003e \u003cp\u003e5.2 Forward Assistance 101\u003c\/p\u003e \u003cp\u003e5.2.1 Adaptive Cruise Control (ACC) and Forward Collision Avoidance (FCA) 101\u003c\/p\u003e \u003cp\u003e5.2.2 Traffic Sign Recognition (TSR) 103\u003c\/p\u003e \u003cp\u003e5.2.3 Traffic Jam Assist (TJA) 105\u003c\/p\u003e \u003cp\u003e5.2.4 Vulnerable Road User Protection 106\u003c\/p\u003e \u003cp\u003e5.2.5 Intelligent Headlamp Control 109\u003c\/p\u003e \u003cp\u003e5.2.6 Enhanced Night Vision (Dynamic Light Spot) 110\u003c\/p\u003e \u003cp\u003e5.2.7 Intelligent Active Suspension 111\u003c\/p\u003e \u003cp\u003e5.3 Lateral Assistance 112\u003c\/p\u003e \u003cp\u003e5.3.1 Lane Departure Warning (LDW) and Lane Keeping System (LKS) 112\u003c\/p\u003e \u003cp\u003e5.3.2 Lane Change Assistance (LCA) 115\u003c\/p\u003e \u003cp\u003e5.3.3 Parking Assistance 116\u003c\/p\u003e \u003cp\u003e5.4 Inside Assistance 117\u003c\/p\u003e \u003cp\u003e5.4.1 Driver Monitoring and Drowsiness Detection 117\u003c\/p\u003e \u003cp\u003e5.5 Conclusions and Future Challenges 119\u003c\/p\u003e \u003cp\u003e5.5.1 Robustness 119\u003c\/p\u003e \u003cp\u003e5.5.2 Cost 121\u003c\/p\u003e \u003cp\u003eAcknowledgments 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Application Challenges from a Bird’s-Eye View 122\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDavide Scaramuzza\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction to Micro Aerial Vehicles (MAVs) 122\u003c\/p\u003e \u003cp\u003e6.1.1 Micro Aerial Vehicles (MAVs) 122\u003c\/p\u003e \u003cp\u003e6.1.2 Rotorcraft MAVs 123\u003c\/p\u003e \u003cp\u003e6.2 GPS-Denied Navigation 124\u003c\/p\u003e \u003cp\u003e6.2.1 Autonomous Navigation with Range Sensors 124\u003c\/p\u003e \u003cp\u003e6.2.2 Autonomous Navigation with Vision Sensors 125\u003c\/p\u003e \u003cp\u003e6.2.3 SFLY: Swarm of Micro Flying Robots 126\u003c\/p\u003e \u003cp\u003e6.2.4 SVO, a Visual-Odometry Algorithm for MAVs 126\u003c\/p\u003e \u003cp\u003e6.3 Applications and Challenges 127\u003c\/p\u003e \u003cp\u003e6.3.1 Applications 127\u003c\/p\u003e \u003cp\u003e6.3.2 Safety and Robustness 128\u003c\/p\u003e \u003cp\u003e6.4 Conclusions 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Application Challenges of Underwater Vision 133\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNuno Gracias, Rafael Garcia, Ricard Campos, Natalia Hurtos, Ricard Prados, ASM Shihavuddin, Tudor Nicosevici, Armagan Elibol, Laszlo Neumann and Javier Escartin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 133\u003c\/p\u003e \u003cp\u003e7.2 Offline Computer Vision Techniques for Underwater Mapping and Inspection 134\u003c\/p\u003e \u003cp\u003e7.2.1 2D Mosaicing 134\u003c\/p\u003e \u003cp\u003e7.2.2 2.5D Mapping 144\u003c\/p\u003e \u003cp\u003e7.2.3 3D Mapping 146\u003c\/p\u003e \u003cp\u003e7.2.4 Machine Learning for Seafloor Classification 154\u003c\/p\u003e \u003cp\u003e7.3 Acoustic Mapping Techniques 157\u003c\/p\u003e \u003cp\u003e7.4 Concluding Remarks 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Closing Notes 161\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAntonio M. López\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eReferences 164\u003c\/p\u003e \u003cp\u003eIndex 195\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406937661783,"sku":"9781118868072","price":67.46,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118868072.jpg?v=1730497618"},{"product_id":"object-detection-by-stereo-vision-images-9781119842194","title":"Object Detection by Stereo Vision Images","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eOBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages,\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Data Conditioning for Medical Imaging 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShahzia Sayyad, Deepti Nikumbh, Dhruvi Lalit Jain, Prachi Dhiren Khatri, Alok Saratchandra Panda and Rupesh Ravindra Joshi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Importance of Image Preprocessing 2\u003c\/p\u003e \u003cp\u003e1.3 Introduction to Digital Medical Imaging 3\u003c\/p\u003e \u003cp\u003e1.3.1 Types of Medical Images for Screening 4\u003c\/p\u003e \u003cp\u003e1.3.1.1 X-rays 4\u003c\/p\u003e \u003cp\u003e1.3.1.2 Computed Tomography (CT) Scan 4\u003c\/p\u003e \u003cp\u003e1.3.1.3 Ultrasound 4\u003c\/p\u003e \u003cp\u003e1.3.1.4 Magnetic Resonance Imaging (MRI) 5\u003c\/p\u003e \u003cp\u003e1.3.1.5 Positron Emission Tomography (PET) Scan 5\u003c\/p\u003e \u003cp\u003e1.3.1.6 Mammogram 5\u003c\/p\u003e \u003cp\u003e1.3.1.7 Fluoroscopy 5\u003c\/p\u003e \u003cp\u003e1.3.1.8 Infrared Thermography 6\u003c\/p\u003e \u003cp\u003e1.4 Preprocessing Techniques of Medical Imaging Using Python 6\u003c\/p\u003e \u003cp\u003e1.4.1 Medical Image Preprocessing 6\u003c\/p\u003e \u003cp\u003e1.4.1.1 Reading the Image 7\u003c\/p\u003e \u003cp\u003e1.4.1.2 Resizing the Image 7\u003c\/p\u003e \u003cp\u003e1.4.1.3 Noise Removal 8\u003c\/p\u003e \u003cp\u003e1.4.1.4 Filtering and Smoothing 9\u003c\/p\u003e \u003cp\u003e1.4.1.5 Image Segmentation 11\u003c\/p\u003e \u003cp\u003e1.5 Medical Image Processing Using Python 13\u003c\/p\u003e \u003cp\u003e1.5.1 Medical Image Processing Methods 16\u003c\/p\u003e \u003cp\u003e1.5.1.1 Image Formation 17\u003c\/p\u003e \u003cp\u003e1.5.1.2 Image Enhancement 19\u003c\/p\u003e \u003cp\u003e1.5.1.3 Image Analysis 19\u003c\/p\u003e \u003cp\u003e1.5.1.4 Image Visualization 19\u003c\/p\u003e \u003cp\u003e1.5.1.5 Image Management 19\u003c\/p\u003e \u003cp\u003e1.6 Feature Extraction Using Python 20\u003c\/p\u003e \u003cp\u003e1.7 Case Study on Throat Cancer 24\u003c\/p\u003e \u003cp\u003e1.7.1 Introduction 24\u003c\/p\u003e \u003cp\u003e1.7.1.1 HSI System 25\u003c\/p\u003e \u003cp\u003e1.7.1.2 The Adaptive Deep Learning Method Proposed 25\u003c\/p\u003e \u003cp\u003e1.7.2 Results and Findings 27\u003c\/p\u003e \u003cp\u003e1.7.3 Discussion 28\u003c\/p\u003e \u003cp\u003e1.7.4 Conclusion 29\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 29\u003c\/p\u003e \u003cp\u003eReferences 30\u003c\/p\u003e \u003cp\u003eAdditional Reading 31\u003c\/p\u003e \u003cp\u003eKey Terms and Definition 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study 33\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje and Anant Sutar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 33\u003c\/p\u003e \u003cp\u003e2.2 Literature Review 35\u003c\/p\u003e \u003cp\u003e2.3 Learning Methods 41\u003c\/p\u003e \u003cp\u003e2.3.1 Machine Learning 41\u003c\/p\u003e \u003cp\u003e2.3.2 Deep Learning 42\u003c\/p\u003e \u003cp\u003e2.3.3 Transfer Learning 42\u003c\/p\u003e \u003cp\u003e2.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques 43\u003c\/p\u003e \u003cp\u003e2.4.1 Dataset Description 43\u003c\/p\u003e \u003cp\u003e2.4.2 Evaluation Platform 44\u003c\/p\u003e \u003cp\u003e2.4.3 Training Process 44\u003c\/p\u003e \u003cp\u003e2.4.4 Model Evaluation of CNN Classifier 46\u003c\/p\u003e \u003cp\u003e2.4.5 Mathematical Model 47\u003c\/p\u003e \u003cp\u003e2.4.6 Parameter Optimization 47\u003c\/p\u003e \u003cp\u003e2.4.7 Performance Metrics 50\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 52\u003c\/p\u003e \u003cp\u003eReferences 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Contamination Monitoring System Using IOT and GIS 57\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKavita R. Singh, Ravi Wasalwar, Ajit Dharmik and Deepshikha Tiwari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 58\u003c\/p\u003e \u003cp\u003e3.2 Literature Survey 58\u003c\/p\u003e \u003cp\u003e3.3 Proposed Work 60\u003c\/p\u003e \u003cp\u003e3.4 Experimentation and Results 61\u003c\/p\u003e \u003cp\u003e3.4.1 Experimental Setup 61\u003c\/p\u003e \u003cp\u003e3.5 Results 64\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 70\u003c\/p\u003e \u003cp\u003eAcknowledgement 71\u003c\/p\u003e \u003cp\u003eReferences 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Video Error Concealment Using Particle Swarm Optimization 73\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajani P. K. and Arti Khaparde\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 74\u003c\/p\u003e \u003cp\u003e4.2 Proposed Research Work Overview 75\u003c\/p\u003e \u003cp\u003e4.3 Error Detection 75\u003c\/p\u003e \u003cp\u003e4.4 Frame Replacement Video Error Concealment Algorithm 77\u003c\/p\u003e \u003cp\u003e4.5 Research Methodology 77\u003c\/p\u003e \u003cp\u003e4.5.1 Particle Swarm Optimization 78\u003c\/p\u003e \u003cp\u003e4.5.2 Spatio-Temporal Video Error Concealment Method 78\u003c\/p\u003e \u003cp\u003e4.5.3 Proposed Modified Particle Swarm Optimization Algorithm 79\u003c\/p\u003e \u003cp\u003e4.6 Results and Analysis 83\u003c\/p\u003e \u003cp\u003e4.6.1 Single Frame With Block Error Analysis 85\u003c\/p\u003e \u003cp\u003e4.6.2 Single Frame With Random Error Analysis 86\u003c\/p\u003e \u003cp\u003e4.6.3 Multiple Frame Error Analysis 88\u003c\/p\u003e \u003cp\u003e4.6.4 Sequential Frame Error Analysis 91\u003c\/p\u003e \u003cp\u003e4.6.5 Subjective Video Quality Analysis for Color Videos 93\u003c\/p\u003e \u003cp\u003e4.6.6 Scene Change of Videos 94\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 95\u003c\/p\u003e \u003cp\u003e4.8 Future Scope 97\u003c\/p\u003e \u003cp\u003eReferences 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Enhanced Image Fusion with Guided Filters 99\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNalini Jagtap and Sudeep D. Thepade\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 100\u003c\/p\u003e \u003cp\u003e5.2 Related Works 100\u003c\/p\u003e \u003cp\u003e5.3 Proposed Methodology 102\u003c\/p\u003e \u003cp\u003e5.3.1 System Model 102\u003c\/p\u003e \u003cp\u003e5.3.2 Steps of the Proposed Methodology 104\u003c\/p\u003e \u003cp\u003e5.4 Experimental Results 104\u003c\/p\u003e \u003cp\u003e5.4.1 Entropy 104\u003c\/p\u003e \u003cp\u003e5.4.2 Peak Signal-to-Noise Ratio 105\u003c\/p\u003e \u003cp\u003e5.4.3 Root Mean Square Error 107\u003c\/p\u003e \u003cp\u003e5.4.3.1 Qab\/f 108\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 108\u003c\/p\u003e \u003cp\u003eReferences 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Deepfake Detection Using LSTM-Based Neural Network 111\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTejaswini Yesugade, Shrikant Kokate, Sarjana Patil, Ritik Varma and Sejal Pawar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 111\u003c\/p\u003e \u003cp\u003e6.2 Related Work 112\u003c\/p\u003e \u003cp\u003e6.2.1 Deepfake Generation 112\u003c\/p\u003e \u003cp\u003e6.2.2 LSTM and CNN 112\u003c\/p\u003e \u003cp\u003e6.3 Existing System 113\u003c\/p\u003e \u003cp\u003e6.3.1 AI-Generated Fake Face Videos by Detecting Eye Blinking 113\u003c\/p\u003e \u003cp\u003e6.3.2 Detection Using Inconsistence in Head Pose 113\u003c\/p\u003e \u003cp\u003e6.3.3 Exploiting Visual Artifacts 113\u003c\/p\u003e \u003cp\u003e6.4 Proposed System 114\u003c\/p\u003e \u003cp\u003e6.4.1 Dataset 114\u003c\/p\u003e \u003cp\u003e6.4.2 Preprocessing 114\u003c\/p\u003e \u003cp\u003e6.4.3 Model 115\u003c\/p\u003e \u003cp\u003e6.5 Results 117\u003c\/p\u003e \u003cp\u003e6.6 Limitations 119\u003c\/p\u003e \u003cp\u003e6.7 Application 119\u003c\/p\u003e \u003cp\u003e6.8 Conclusion 119\u003c\/p\u003e \u003cp\u003eReferences 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Classification of Fetal Brain Abnormalities with MRI Images: A Survey 121\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKavita Shinde and Anuradha Thakare\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 121\u003c\/p\u003e \u003cp\u003e7.2 Related Work 123\u003c\/p\u003e \u003cp\u003e7.3 Evaluation of Related Research 129\u003c\/p\u003e \u003cp\u003e7.4 General Framework for Fetal Brain Abnormality Classification 129\u003c\/p\u003e \u003cp\u003e7.4.1 Image Acquisition 130\u003c\/p\u003e \u003cp\u003e7.4.2 Image Pre-Processing 130\u003c\/p\u003e \u003cp\u003e7.4.2.1 Image Thresholding 130\u003c\/p\u003e \u003cp\u003e7.4.2.2 Morphological Operations 131\u003c\/p\u003e \u003cp\u003e7.4.2.3 Hole Filling and Mask Generation 131\u003c\/p\u003e \u003cp\u003e7.4.2.4 MRI Segmentation for Fetal Brain Extraction 132\u003c\/p\u003e \u003cp\u003e7.4.3 Feature Extraction 132\u003c\/p\u003e \u003cp\u003e7.4.3.1 Gray-Level Co-Occurrence Matrix 133\u003c\/p\u003e \u003cp\u003e7.4.3.2 Discrete Wavelet Transformation 133\u003c\/p\u003e \u003cp\u003e7.4.3.3 Gabor Filters 134\u003c\/p\u003e \u003cp\u003e7.4.3.4 Discrete Statistical Descriptive Features 134\u003c\/p\u003e \u003cp\u003e7.4.4 Feature Reduction 134\u003c\/p\u003e \u003cp\u003e7.4.4.1 Principal Component Analysis 135\u003c\/p\u003e \u003cp\u003e7.4.4.2 Linear Discriminant Analysis 136\u003c\/p\u003e \u003cp\u003e7.4.4.3 Non-Linear Dimensionality Reduction Techniques 137\u003c\/p\u003e \u003cp\u003e7.4.5 Classification by Using Machine Learning Classifiers 137\u003c\/p\u003e \u003cp\u003e7.4.5.1 Support Vector Machine 138\u003c\/p\u003e \u003cp\u003e7.4.5.2 K-Nearest Neighbors 138\u003c\/p\u003e \u003cp\u003e7.4.5.3 Random Forest 139\u003c\/p\u003e \u003cp\u003e7.4.5.4 Linear Discriminant Analysis 139\u003c\/p\u003e \u003cp\u003e7.4.5.5 Naïve Bayes 139\u003c\/p\u003e \u003cp\u003e7.4.5.6 Decision Tree (DT) 140\u003c\/p\u003e \u003cp\u003e7.4.5.7 Convolutional Neural Network 140\u003c\/p\u003e \u003cp\u003e7.5 Performance Metrics for Research in Fetal Brain Analysis 141\u003c\/p\u003e \u003cp\u003e7.6 Challenges 142\u003c\/p\u003e \u003cp\u003e7.7 Conclusion and Future Works 142\u003c\/p\u003e \u003cp\u003eReferences 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Analysis of COVID-19 Data Using Machine Learning Algorithm 147\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChinnaiah Kotadi, Mithun Chakravarthi K., Srihari Chintha and Kapil Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 147\u003c\/p\u003e \u003cp\u003e8.2 Pre-Processing 148\u003c\/p\u003e \u003cp\u003e8.3 Selecting Features 149\u003c\/p\u003e \u003cp\u003e8.4 Analysis of COVID-19–Confirmed Cases in India 152\u003c\/p\u003e \u003cp\u003e8.4.1 Analysis to Highest COVID-19–Confirmed Case States in India 153\u003c\/p\u003e \u003cp\u003e8.4.2 Analysis to Highest COVID-19 Death Rate States in India 153\u003c\/p\u003e \u003cp\u003e8.4.3 Analysis to Highest COVID-19 Cured Case States in India 154\u003c\/p\u003e \u003cp\u003e8.4.4 Analysis of Daily COVID-19 Cases in Maharashtra State 155\u003c\/p\u003e \u003cp\u003e8.5 Linear Regression Used for Predicting Daily Wise COVID- 19\u003c\/p\u003e \u003cp\u003eCases in Maharashtra 156\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 157\u003c\/p\u003e \u003cp\u003eReferences 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering 159\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eManish Sharma and Rutuja Deshmukh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 160\u003c\/p\u003e \u003cp\u003e9.2 Related Work 162\u003c\/p\u003e \u003cp\u003e9.3 Recommender Systems and Collaborative Filtering 164\u003c\/p\u003e \u003cp\u003e9.4 Proposed Methodology 165\u003c\/p\u003e \u003cp\u003e9.5 Experiment Analysis 167\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 168\u003c\/p\u003e \u003cp\u003eReferences 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Virtual Moratorium System 171\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eManisha Bhende, Muzasarali Badger, Pranish Kumbhar, Vedanti Bhatkar and Payal Chavan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 172\u003c\/p\u003e \u003cp\u003e10.1.1 Objectives 172\u003c\/p\u003e \u003cp\u003e10.2 Literature Survey 172\u003c\/p\u003e \u003cp\u003e10.2.1 Virtual Assistant—BLU 172\u003c\/p\u003e \u003cp\u003e10.2.2 HDFC Ask EVA 173\u003c\/p\u003e \u003cp\u003e10.3 Methodologies of Problem Solving 173\u003c\/p\u003e \u003cp\u003e10.4 Modules 174\u003c\/p\u003e \u003cp\u003e10.4.1 Chatbot 174\u003c\/p\u003e \u003cp\u003e10.4.2 Android Application 175\u003c\/p\u003e \u003cp\u003e10.4.3 Web Application 175\u003c\/p\u003e \u003cp\u003e10.5 Detailed Flow of Proposed Work 176\u003c\/p\u003e \u003cp\u003e10.5.1 System Architecture 176\u003c\/p\u003e \u003cp\u003e10.5.2 DFD Level 1 177\u003c\/p\u003e \u003cp\u003e10.6 Architecture Design 178\u003c\/p\u003e \u003cp\u003e10.6.1 Main Server 178\u003c\/p\u003e \u003cp\u003e10.6.2 Chatbot 178\u003c\/p\u003e \u003cp\u003e10.6.3 Database Architecture 180\u003c\/p\u003e \u003cp\u003e10.6.4 Web Scraper 180\u003c\/p\u003e \u003cp\u003e10.7 Algorithms Used 181\u003c\/p\u003e \u003cp\u003e10.7.1 AES-256 Algorithm 181\u003c\/p\u003e \u003cp\u003e10.7.2 Rasa NLU 181\u003c\/p\u003e \u003cp\u003e10.8 Results 182\u003c\/p\u003e \u003cp\u003e10.9 Discussions 183\u003c\/p\u003e \u003cp\u003e10.9.1 Applications 183\u003c\/p\u003e \u003cp\u003e10.9.2 Future Work 183\u003c\/p\u003e \u003cp\u003e10.9.3 Conclusion 183\u003c\/p\u003e \u003cp\u003eReferences 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Efficient Land Cover Classification for Urban Planning 185\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVandana Tulshidas Chavan and Sanjeev J. Wagh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 185\u003c\/p\u003e \u003cp\u003e11.2 Literature Survey 189\u003c\/p\u003e \u003cp\u003e11.3 Proposed Methodology 191\u003c\/p\u003e \u003cp\u003e11.4 Conclusion 192\u003c\/p\u003e \u003cp\u003eReferences 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Data-Driven Approches for Fake News Detection on Social Media Platforms: Review 195\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePradnya Patil and Sanjeev J. Wagh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 196\u003c\/p\u003e \u003cp\u003e12.2 Literature Survey 196\u003c\/p\u003e \u003cp\u003e12.3 Problem Statement and Objectives 201\u003c\/p\u003e \u003cp\u003e12.3.1 Problem Statement 201\u003c\/p\u003e \u003cp\u003e12.3.2 Objectives 201\u003c\/p\u003e \u003cp\u003e12.4 Proposed Methodology 202\u003c\/p\u003e \u003cp\u003e12.4.1 Pre-Processing 202\u003c\/p\u003e \u003cp\u003e12.4.2 Feature Extraction 203\u003c\/p\u003e \u003cp\u003e12.4.3 Classification 203\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 204\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering 207\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnupama Patil, Manisha Bhende, Suvarna Patil and P. P. Shevatekar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 208\u003c\/p\u003e \u003cp\u003e13.2 Related Work 210\u003c\/p\u003e \u003cp\u003e13.3 Distance Measurement Using Stereo Vision 213\u003c\/p\u003e \u003cp\u003e13.3.1 Calibration of the Camera 215\u003c\/p\u003e \u003cp\u003e13.3.2 Stereo Image Rectification 215\u003c\/p\u003e \u003cp\u003e13.3.3 Disparity Estimation and Stereo Matching 216\u003c\/p\u003e \u003cp\u003e13.3.4 Measurement of Distance 217\u003c\/p\u003e \u003cp\u003e13.4 Object Segmentation in Depth Map 218\u003c\/p\u003e \u003cp\u003e13.4.1 Formation of Depth Map 218\u003c\/p\u003e \u003cp\u003e13.4.2 Density-Based in 3D Object Grouping Clustering 218\u003c\/p\u003e \u003cp\u003e13.4.3 Layered Images Object Segmentation 219\u003c\/p\u003e \u003cp\u003e13.4.3.1 Image Layer Formation 221\u003c\/p\u003e \u003cp\u003e13.4.3.2 Determination of Object Boundaries 222\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 223\u003c\/p\u003e \u003cp\u003eReferences 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Real-Time Depth Estimation Using BLOB Detection\/ Contour Detection 227\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArokia Priya Charles, Anupama V. Patil and Sunil Dambhare\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 227\u003c\/p\u003e \u003cp\u003e14.2 Estimation of Depth Using Blob Detection 229\u003c\/p\u003e \u003cp\u003e14.2.1 Grayscale Conversion 230\u003c\/p\u003e \u003cp\u003e14.2.2 Thresholding 231\u003c\/p\u003e \u003cp\u003e14.2.3 Image Subtraction in Case of Input with Background 232\u003c\/p\u003e \u003cp\u003e14.2.3.1 Preliminaries 233\u003c\/p\u003e \u003cp\u003e14.2.3.2 Computing Time 234\u003c\/p\u003e \u003cp\u003e14.3 Blob 234\u003c\/p\u003e \u003cp\u003e14.3.1 BLOB Extraction 234\u003c\/p\u003e \u003cp\u003e14.3.2 Blob Classification 235\u003c\/p\u003e \u003cp\u003e14.3.2.1 Image Moments 236\u003c\/p\u003e \u003cp\u003e14.3.2.2 Centroid Using Image Moments 238\u003c\/p\u003e \u003cp\u003e14.3.2.3 Central Moments 238\u003c\/p\u003e \u003cp\u003e14.4 Challenges 241\u003c\/p\u003e \u003cp\u003e14.5 Experimental Results 241\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 251\u003c\/p\u003e \u003cp\u003eReferences 255\u003c\/p\u003e \u003cp\u003eIndex 257\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407169036631,"sku":"9781119842194","price":118.4,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119842194.jpg?v=1730498413"},{"product_id":"image-segmentation-principles-techniques-and-applications-9781119859000","title":"Image Segmentation  Principles Techniques and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eImage Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field  The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture.  Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authorssuch as convolutional neural networks, graph convolutional networks, deformable convolution, and model compressionto assist graduate students and researchers apply and improve image segmentation in their work.  Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology.   Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory.   Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc.   Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc.    Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003eAbout the Authors\u003c\/p\u003e \u003cp\u003eList of Abbreviations\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One: Principle \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1   Introduction to Image Segmentation\u003c\/p\u003e \u003cp\u003e2   Principles of Clustering\u003c\/p\u003e \u003cp\u003e3   Principles of Mathematical Morphology\u003c\/p\u003e \u003cp\u003e4   Principles of Neural Network\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two: Methods\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5   Fast and Robust Image Segmentation Using Clustering\u003c\/p\u003e \u003cp\u003e6   Fast Image Segmentation Using Watershed Transform\u003c\/p\u003e \u003cp\u003e7   Superpixel-based Fast Image Segmentation\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three:  Application\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8   Image Segmentation for Traffic Scene Analysis\u003c\/p\u003e \u003cp\u003e9   Image Segmentation for Medical Analysis\u003c\/p\u003e \u003cp\u003e10 Image Segmentation for Remote Sensing Analysis\u003c\/p\u003e \u003cp\u003e11 Image Segmentation for Material Analysis\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407175983447,"sku":"9781119859000","price":99.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119859000.jpg?v=1730498438"},{"product_id":"machine-learning-applications-9781394173327","title":"Machine Learning Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMachine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader's active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space explor","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407597019479,"sku":"9781394173327","price":88.65,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781394173327.jpg?v=1730499882"},{"product_id":"introduction-to-biometrics-9781489985439","title":"Introduction to Biometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWhile the deployment of large-scale biometric systems in both commercial and government applications has increased the public awareness of this technology, \"Introduction to Biometrics\" is the first textbook to introduce the fundamentals of Biometrics to undergraduate\/graduate students.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":49409184104791,"sku":"9781489985439","price":49.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781489985439.jpg?v=1730505825"},{"product_id":"learning-opencv-3-9781491937990","title":"Learning OpenCV 3","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eGet started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49409190232407,"sku":"9781491937990","price":50.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781491937990.jpg?v=1730505852"},{"product_id":"change-detection-and-image-time-series-analysis-1-unervised-methods-9781789450569","title":"Change Detection and Image Time-Series Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eChange Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and\/or synthetic aperture radar acquisition modalities. \u003cbr\u003e\u003cbr\u003eChapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature\/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eContents\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE\u003c\/p\u003e \u003cp\u003eList of Notations\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Unsupervised Change Detection in Multitemporal Remote Sensing Images 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, QianDU\u003c\/p\u003e \u003cp\u003eand Xiaohua TONG\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003c\/p\u003e \u003cp\u003e1.2. Unsupervised change detection in multispectral images 3\u003c\/p\u003e \u003cp\u003e1.2.1.Relatedconcepts 3\u003c\/p\u003e \u003cp\u003e1.2.2.Openissuesandchallenges 7\u003c\/p\u003e \u003cp\u003e1.2.3. Spectral–spatial unsupervised CD techniques 7\u003c\/p\u003e \u003cp\u003e1.3 Unsupervised multiclass change detection approaches based on modelingspectral–spatialinformation 9\u003c\/p\u003e \u003cp\u003e1.3.1 Sequential spectral change vector analysis (S 2 CVA) 9\u003c\/p\u003e \u003cp\u003e1.3.2. Multiscale morphological compressed change vector analysis 11\u003c\/p\u003e \u003cp\u003e1.3.3. Superpixel-level compressed change vector analysis 15\u003c\/p\u003e \u003cp\u003e1.4.Datasetdescriptionandexperimentalsetup 18\u003c\/p\u003e \u003cp\u003e1.4.1.Datasetdescription 18\u003c\/p\u003e \u003cp\u003e1.4.2.Experimentalsetup 22\u003c\/p\u003e \u003cp\u003e1.5.Resultsanddiscussion 24\u003c\/p\u003e \u003cp\u003e1.5.1.ResultsontheXuzhoudataset 24\u003c\/p\u003e \u003cp\u003e1.5.2. Results on the Indonesia tsunami dataset 24\u003c\/p\u003e \u003cp\u003exv\u003c\/p\u003e \u003cp\u003e1.6.Conclusion 28\u003c\/p\u003e \u003cp\u003e1.7.Acknowledgements 29\u003c\/p\u003e \u003cp\u003e1.8.References 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Change Detection in Time Series of Polarimetric SAR Images 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKnut CONRADSEN, Henning SKRIVER, MortonJ.CANTY\u003c\/p\u003e \u003cp\u003eandAllanA.NIELSEN\u003c\/p\u003e \u003cp\u003e2.1. Introduction 35\u003c\/p\u003e \u003cp\u003e2.1.1.Theproblem 36\u003c\/p\u003e \u003cp\u003e2.1.2 Important concepts illustrated by means of the gamma distribution 39\u003c\/p\u003e \u003cp\u003e2.2.Testtheoryandmatrixordering 45\u003c\/p\u003e \u003cp\u003e2.2.1. Test for equality of two complex Wishart distributions 45\u003c\/p\u003e \u003cp\u003e2.2.2. Test for equality of k-complex Wishart distributions 47\u003c\/p\u003e \u003cp\u003e2.2.3. The block diagonal case 49\u003c\/p\u003e \u003cp\u003e2.2.4.TheLoewnerorder 52\u003c\/p\u003e \u003cp\u003e2.3.Thebasicchangedetectionalgorithm 53\u003c\/p\u003e \u003cp\u003e2.4.Applications 55\u003c\/p\u003e \u003cp\u003e2.4.1.Visualizingchanges 58\u003c\/p\u003e \u003cp\u003e2.4.2.Fieldwisechangedetection 59\u003c\/p\u003e \u003cp\u003e2.4.3. Directional changes using the Loewner ordering 62\u003c\/p\u003e \u003cp\u003e2.4.4. Software availability 65\u003c\/p\u003e \u003cp\u003e2.5.References 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAmmar MIAN, Guillaume GINOLHAC, Jean-Philippe OVARLEZ,\u003c\/p\u003e \u003cp\u003eArnaud BRELOY and Frédéric PASCAL\u003c\/p\u003e \u003cp\u003e3.1. Introduction 73\u003c\/p\u003e \u003cp\u003e3.2.Datasetdescription 76\u003c\/p\u003e \u003cp\u003e3.3.StatisticalmodelingofSARimages 77\u003c\/p\u003e \u003cp\u003e3.3.1.Thedata 77\u003c\/p\u003e \u003cp\u003e3.3.2.Gaussianmodel 77\u003c\/p\u003e \u003cp\u003e3.3.3.Non-Gaussianmodeling 83\u003c\/p\u003e \u003cp\u003e3.4.Dissimilaritymeasures 84\u003c\/p\u003e \u003cp\u003e3.4.1.Problemformulation 84\u003c\/p\u003e \u003cp\u003e3.4.2. Hypothesis testing statistics 85\u003c\/p\u003e \u003cp\u003e3.4.3.Information-theoreticmeasures 87\u003c\/p\u003e \u003cp\u003e3.4.4.Riemanniangeometrydistances 89\u003c\/p\u003e \u003cp\u003e3.4.5.Optimaltransport 90\u003c\/p\u003e \u003cp\u003e3.4.6.Summary 91\u003c\/p\u003e \u003cp\u003e3.4.7. Results of change detectors on the UAVSAR dataset 91\u003c\/p\u003e \u003cp\u003e3.5. Change detection based on structured covariances 94\u003c\/p\u003e \u003cp\u003e3.5.1. Low-rank Gaussian change detector 96\u003c\/p\u003e \u003cp\u003e3.5.2. Low-rank compound Gaussian change detector 97\u003c\/p\u003e \u003cp\u003e3.5.3. Results of low-rank change detectors on the UAVSAR dataset 100\u003c\/p\u003e \u003cp\u003e3.6.Conclusion 102\u003c\/p\u003e \u003cp\u003e3.7.References 103\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO, Fatima KARBOU, Sophie GIFFARD-ROISIN\u003c\/p\u003e \u003cp\u003eand Lionel BOMBRUN\u003c\/p\u003e \u003cp\u003e4.1. Introduction 109\u003c\/p\u003e \u003cp\u003e4.2.Parametricmodelingofconvnetfeatures 110\u003c\/p\u003e \u003cp\u003e4.3.Anomalydetectioninimagetimeseries 113\u003c\/p\u003e \u003cp\u003e4.4.Functionalimagetimeseriesclustering 119\u003c\/p\u003e \u003cp\u003e4.5.Conclusion 123\u003c\/p\u003e \u003cp\u003e4.6.References 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFatima KARBOU, Guillaume JAMES, Philippe DURAND\u003c\/p\u003e \u003cp\u003eand Abdourrahmane M. ATTO\u003c\/p\u003e \u003cp\u003e5.1. Introduction 127\u003c\/p\u003e \u003cp\u003e5.2.Testareaanddata 129\u003c\/p\u003e \u003cp\u003e5.3.WetsnowdetectionusingSentinel-1 129\u003c\/p\u003e \u003cp\u003e5.4.Metricstodetectwetsnow 133\u003c\/p\u003e \u003cp\u003e5.5.Discussion 138\u003c\/p\u003e \u003cp\u003e5.6.Conclusion 143\u003c\/p\u003e \u003cp\u003e5.7.Acknowledgements 143\u003c\/p\u003e \u003cp\u003e5.8.References 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO, Aluísio PINHEIRO, Guillaume GINOLHAC\u003c\/p\u003e \u003cp\u003eand Pedro MORETTIN\u003c\/p\u003e \u003cp\u003e6.1. Introduction 145\u003c\/p\u003e \u003cp\u003e6.2. Random field model of a cyclone texture 148\u003c\/p\u003e \u003cp\u003e6.2.1.Cyclonetexturefeature 149\u003c\/p\u003e \u003cp\u003e6.2.2. Wavelet-based power spectral densities and cyclone fields 150\u003c\/p\u003e \u003cp\u003e6.2.3. Fractional spectral power decay model 153\u003c\/p\u003e \u003cp\u003e6.3.Cyclonefieldeyedetectionandtracking 157\u003c\/p\u003e \u003cp\u003e6.3.1.Cycloneeyedetection 157\u003c\/p\u003e \u003cp\u003e6.3.2.Dynamicfractalfieldeyetracking 158\u003c\/p\u003e \u003cp\u003e6.4. Cyclone field intensity evolution prediction 159\u003c\/p\u003e \u003cp\u003e6.5.Discussion 161\u003c\/p\u003e \u003cp\u003e6.6.Acknowledgements 163\u003c\/p\u003e \u003cp\u003e6.7.References 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMinh-Tan PHAM and Grégoire MERCIER\u003c\/p\u003e \u003cp\u003e7.1. Introduction 167\u003c\/p\u003e \u003cp\u003e7.2. Texture representation and characterization using local extrema 169\u003c\/p\u003e \u003cp\u003e7.2.1.Motivationandapproach 169\u003c\/p\u003e \u003cp\u003e7.2.2. Local extrema keypoints within SAR images 172\u003c\/p\u003e \u003cp\u003e7.3.Unsupervisedchangedetection 175\u003c\/p\u003e \u003cp\u003e7.3.1. Proposed framework 175\u003c\/p\u003e \u003cp\u003e7.3.2. Weighted graph construction from keypoints 176\u003c\/p\u003e \u003cp\u003e7.3.3.Changemeasure(CM)generation 178\u003c\/p\u003e \u003cp\u003e7.4.Experimentalstudy 179\u003c\/p\u003e \u003cp\u003e7.4.1. Data description and evaluation criteria 179\u003c\/p\u003e \u003cp\u003e7.4.2.Changedetectionresults 181\u003c\/p\u003e \u003cp\u003e7.4.3.Sensitivitytoparameters 185\u003c\/p\u003e \u003cp\u003e7.4.4.ComparisonwiththeNLMmodel 188\u003c\/p\u003e \u003cp\u003e7.4.5. Analysis of the algorithm complexity 191\u003c\/p\u003e \u003cp\u003e7.5.Applicationtoglacierflowmeasurement 192\u003c\/p\u003e \u003cp\u003e7.5.1. Proposed method 193\u003c\/p\u003e \u003cp\u003e7.5.2.Results 194\u003c\/p\u003e \u003cp\u003e7.6.Conclusion 196\u003c\/p\u003e \u003cp\u003e7.7.References 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Multitemporal Analysis of Sentinel-1\/2 Images for Land Use Monitoring at Regional Scale 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAndrea GARZELLI and Claudia ZOPPETTI\u003c\/p\u003e \u003cp\u003e8.1. Introduction 201\u003c\/p\u003e \u003cp\u003e8.2. Proposed method 203\u003c\/p\u003e \u003cp\u003e8.2.1.Testsiteanddata 206\u003c\/p\u003e \u003cp\u003e8.3.SARprocessing 209\u003c\/p\u003e \u003cp\u003e8.4.Opticalprocessing 215\u003c\/p\u003e \u003cp\u003e8.5.Combinationlayer 217\u003c\/p\u003e \u003cp\u003e8.6.Results 219\u003c\/p\u003e \u003cp\u003e8.7.Conclusion 220\u003c\/p\u003e \u003cp\u003e8.8.References 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Statistical Difference Models for Change Detection in Multispectral Images 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMassimo ZANETTI, Francesca BOVOLO and Lorenzo BRUZZONE\u003c\/p\u003e \u003cp\u003e9.1. Introduction 223\u003c\/p\u003e \u003cp\u003e9.2. Overview of the change detection problem 225\u003c\/p\u003e \u003cp\u003e9.2.1. Change detection methods for multispectral images 227\u003c\/p\u003e \u003cp\u003e9.2.2. Challenges addressed in this chapter 230\u003c\/p\u003e \u003cp\u003e9.3 The Rayleigh–Rice mixture model for the magnitude of the differenceimage 231\u003c\/p\u003e \u003cp\u003e9.3.1. Magnitude image statistical mixture model 231\u003c\/p\u003e \u003cp\u003e9.3.2.Bayesiandecision 233\u003c\/p\u003e \u003cp\u003e9.3.3. Numerical approach to parameter estimation 234\u003c\/p\u003e \u003cp\u003e9.4. A compound multiclass statistical model of the difference image 239\u003c\/p\u003e \u003cp\u003e9.4.1. Difference image statistical mixture model 240\u003c\/p\u003e \u003cp\u003e9.4.2. Magnitude image statistical mixture model 245\u003c\/p\u003e \u003cp\u003e9.4.3.Bayesiandecision 248\u003c\/p\u003e \u003cp\u003e9.4.4. Numerical approach to parameter estimation 249\u003c\/p\u003e \u003cp\u003e9.5.Experimentalresults 253\u003c\/p\u003e \u003cp\u003e9.5.1.Datasetdescription 253\u003c\/p\u003e \u003cp\u003e9.5.2.Experimentalsetup 256\u003c\/p\u003e \u003cp\u003e9.5.3. Test 1: Two-class Rayleigh–Rice mixture model 256\u003c\/p\u003e \u003cp\u003e9.5.4. Test 2: Multiclass Rician mixture model 260\u003c\/p\u003e \u003cp\u003e9.6.Conclusion 266\u003c\/p\u003e \u003cp\u003e9.7.References 267\u003c\/p\u003e \u003cp\u003eList of Authors 275\u003c\/p\u003e \u003cp\u003eIndex 277\u003c\/p\u003e \u003cp\u003eSummary of Volume 2 281\u003c\/p\u003e","brand":"ISTE Ltd","offers":[{"title":"Default Title","offer_id":49412637917527,"sku":"9781789450569","price":124.15,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781789450569.jpg?v=1730517446"},{"product_id":"change-detection-and-image-time-series-analysis-2-supervised-methods-9781789450576","title":"Change Detection and Image Time Series Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChange Detection and Image Time Series Analysis 2\u003c\/i\u003e presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.\u003cbr\u003e\u003cbr\u003eChapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.\u003cbr\u003e\u003cbr\u003eChapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.\u003cbr\u003e\u003cbr\u003eChapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,\u003cbr\u003e\u003cbr\u003eChapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and\/or multilabel change classification issues.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eContents\u003c\/p\u003e \u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE\u003c\/p\u003e \u003cp\u003eList of Notations\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIhsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO\u003c\/p\u003e \u003cp\u003eand Josiane ZERUBIA\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003c\/p\u003e \u003cp\u003e1.1.1. The role of multisensor data in time series classification 1\u003c\/p\u003e \u003cp\u003e1.1.2. Multisensor and multiresolution classification 2\u003c\/p\u003e \u003cp\u003e1.1.3.Previouswork 5\u003c\/p\u003e \u003cp\u003e1.2. Methodology 9\u003c\/p\u003e \u003cp\u003e1.2.1. Overview of the proposed approaches 9\u003c\/p\u003e \u003cp\u003e1.2.2. Hierarchical model associated with the first proposed method 10\u003c\/p\u003e \u003cp\u003e1.2.3. Hierarchical model associated with the second proposed method 13\u003c\/p\u003e \u003cp\u003e1.2.4. Multisensor hierarchical MPM inference 14\u003c\/p\u003e \u003cp\u003e1.2.5. Probability density estimation through finite mixtures 17\u003c\/p\u003e \u003cp\u003e1.3.Examplesofexperimentalresults 19\u003c\/p\u003e \u003cp\u003e1.3.1.Resultsofthefirstmethod 19\u003c\/p\u003e \u003cp\u003e1.3.2.Resultsofthesecondmethod 22\u003c\/p\u003e \u003cp\u003e1.4.Conclusion 26\u003c\/p\u003e \u003cp\u003exiii\u003c\/p\u003e \u003cp\u003e1.5.Acknowledgments 26\u003c\/p\u003e \u003cp\u003e1.6.References 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCharlotte PELLETIER and Silvia VALERO\u003c\/p\u003e \u003cp\u003e2.1. Introduction 33\u003c\/p\u003e \u003cp\u003e2.2. Basic concepts in supervised remote sensing classification 35\u003c\/p\u003e \u003cp\u003e2.2.1. Preparing data before it is fed into classification algorithms 35\u003c\/p\u003e \u003cp\u003e2.2.2. Key considerations when training supervised classifiers 39\u003c\/p\u003e \u003cp\u003e2.2.3. Performance evaluation of supervised classifiers 41\u003c\/p\u003e \u003cp\u003e2.3.Traditionalclassificationalgorithms 45\u003c\/p\u003e \u003cp\u003e2.3.1. Support vector machines 45\u003c\/p\u003e \u003cp\u003e2.3.2. Random forests 51\u003c\/p\u003e \u003cp\u003e2.3.3. k-nearest neighbor 56\u003c\/p\u003e \u003cp\u003e2.4. Classification strategies based on temporal feature representations 59\u003c\/p\u003e \u003cp\u003e2.4.1. Phenology-based classification approaches 60\u003c\/p\u003e \u003cp\u003e2.4.2 Dictionary-based classificationapproaches 61\u003c\/p\u003e \u003cp\u003e2.4.3 Shapelet-based classificationapproaches 62\u003c\/p\u003e \u003cp\u003e2.5.Deeplearningapproaches 63\u003c\/p\u003e \u003cp\u003e2.5.1. Introduction to deep learning 64\u003c\/p\u003e \u003cp\u003e2.5.2.Convolutionalneuralnetworks 68\u003c\/p\u003e \u003cp\u003e2.5.3.Recurrentneuralnetworks 71\u003c\/p\u003e \u003cp\u003e2.6.References 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Semantic Analysis of Satellite Image Time Series 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCorneliu Octavian DUMITRU and Mihai DATCU\u003c\/p\u003e \u003cp\u003e3.1. Introduction 85\u003c\/p\u003e \u003cp\u003e3.1.1.TypicalSITSexamples 89\u003c\/p\u003e \u003cp\u003e3.1.2. Irregular acquisitions 90\u003c\/p\u003e \u003cp\u003e3.1.3.Thechapterstructure 96\u003c\/p\u003e \u003cp\u003e3.2.WhyaresemanticsneededinSITS? 96\u003c\/p\u003e \u003cp\u003e3.3.Similaritymetrics 97\u003c\/p\u003e \u003cp\u003e3.4. Feature methods 98\u003c\/p\u003e \u003cp\u003e3.5. Classification methods 98\u003c\/p\u003e \u003cp\u003e3.5.1.Activelearning 99\u003c\/p\u003e \u003cp\u003e3.5.2.Relevancefeedback 100\u003c\/p\u003e \u003cp\u003e3.5.3. Compression-based pattern recognition 100\u003c\/p\u003e \u003cp\u003e3.5.4.LatentDirichletallocation 101\u003c\/p\u003e \u003cp\u003e3.6.Conclusion 102\u003c\/p\u003e \u003cp\u003evii\u003c\/p\u003e \u003cp\u003e3.7.Acknowledgments 105\u003c\/p\u003e \u003cp\u003e3.8.References 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMatthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU\u003c\/p\u003e \u003cp\u003e4.1. Introduction 109\u003c\/p\u003e \u003cp\u003e4.2. Annual time series 111\u003c\/p\u003e \u003cp\u003e4.2.1. Overview of annual time series methods 111\u003c\/p\u003e \u003cp\u003e4.2.2 Examples of annual times series analysis applications for environmentalmonitoring 112\u003c\/p\u003e \u003cp\u003e4.2.3.Towardsdensetimeseriesanalysis 116\u003c\/p\u003e \u003cp\u003e4.3. Dense time series analysis using all available data 117\u003c\/p\u003e \u003cp\u003e4.3.1. Making dense time series consistent 118\u003c\/p\u003e \u003cp\u003e4.3.2. Change detection methods 121\u003c\/p\u003e \u003cp\u003e4.3.3.Summaryandfuturedevelopments 125\u003c\/p\u003e \u003cp\u003e4.4. Deep learning-based time series analysis approaches 126\u003c\/p\u003e \u003cp\u003e4.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129\u003c\/p\u003e \u003cp\u003e4.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131\u003c\/p\u003e \u003cp\u003e4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134\u003c\/p\u003e \u003cp\u003e4.4.4. Synthesis and future developments 136\u003c\/p\u003e \u003cp\u003e4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 136\u003c\/p\u003e \u003cp\u003e4.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 137\u003c\/p\u003e \u003cp\u003e4.5.2. Deep learning and computer vision as technology enablers 138\u003c\/p\u003e \u003cp\u003e4.5.3.Futuresteps 139\u003c\/p\u003e \u003cp\u003e4.6.References 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ\u003c\/p\u003e \u003cp\u003e5.1. Introduction 155\u003c\/p\u003e \u003cp\u003e5.1.1. Research methodology and statistics 159\u003c\/p\u003e \u003cp\u003e5.2. Satellite-based earthquake damage assessment 165\u003c\/p\u003e \u003cp\u003e5.3. Pre-processing of satellite images before damage assessment 167\u003c\/p\u003e \u003cp\u003e5.4. Multi-source image analysis 168\u003c\/p\u003e \u003cp\u003e5.5. Contextual feature mining for damage assessment 169\u003c\/p\u003e \u003cp\u003e5.5.1.Texturalfeatures 170\u003c\/p\u003e \u003cp\u003e5.5.2. Filter-based methods 173\u003c\/p\u003e \u003cp\u003e5.6. Multi-temporal image analysis for damage assessment 175\u003c\/p\u003e \u003cp\u003e5.6.1. Use of machine learning in damage assessment problem 176\u003c\/p\u003e \u003cp\u003e5.6.2. Rapid earthquake damage assessment 180\u003c\/p\u003e \u003cp\u003e5.7. Understanding damage following an earthquake using satellite-based SAR 181\u003c\/p\u003e \u003cp\u003e5.7.1. SAR fundamental parameters and acquisition vector 185\u003c\/p\u003e \u003cp\u003e5.7.2. Coherent methods for damage assessment 188\u003c\/p\u003e \u003cp\u003e5.7.3. Incoherent methods for damage assessment 192\u003c\/p\u003e \u003cp\u003e5.7.4. Post-earthquake-only SAR data-based damage assessment 195\u003c\/p\u003e \u003cp\u003e5.7.5 Combination of coherent and incoherent methods for damage assessment 196\u003c\/p\u003e \u003cp\u003e5.7.6.Summary 198\u003c\/p\u003e \u003cp\u003e5.8. Use of auxiliary data sources 200\u003c\/p\u003e \u003cp\u003e5.9.Damagegrades 200\u003c\/p\u003e \u003cp\u003e5.10.Conclusionanddiscussion 203\u003c\/p\u003e \u003cp\u003e5.11.References 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIER\u003c\/p\u003e \u003cp\u003eand Emmanuel TROUVÉ\u003c\/p\u003e \u003cp\u003e6.1. Introduction 223\u003c\/p\u003e \u003cp\u003e6.2. Coarse- to fine-grained change of state dataset 225\u003c\/p\u003e \u003cp\u003e6.3. Deep transfer learning models for change of state classification 232\u003c\/p\u003e \u003cp\u003e6.3.1.Deeplearningmodellibrary 232\u003c\/p\u003e \u003cp\u003e6.3.2.GraphstructuresfortheCNNlibrary 234\u003c\/p\u003e \u003cp\u003e6.3.3. Dimensionalities of the learnables for the CNN library 236\u003c\/p\u003e \u003cp\u003e6.4.Changeofstateanalysis 237\u003c\/p\u003e \u003cp\u003e6.4.1 Transfer learning adaptations for the change of state classificationissues 238\u003c\/p\u003e \u003cp\u003e6.4.2.Experimentalresults 239\u003c\/p\u003e \u003cp\u003e6.5.Conclusion 243\u003c\/p\u003e \u003cp\u003e6.6.Acknowledgments 244\u003c\/p\u003e \u003cp\u003e6.7.References 244\u003c\/p\u003e \u003cp\u003eList of Authors 247\u003c\/p\u003e \u003cp\u003eIndex 249\u003c\/p\u003e \u003cp\u003eSummary of Volume 1 253\u003c\/p\u003e","brand":"ISTE Ltd","offers":[{"title":"Default Title","offer_id":49412637983063,"sku":"9781789450576","price":124.15,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781789450576.jpg?v=1730517446"},{"product_id":"face-analysis-under-uncontrolled-conditions-from-face-detection-to-expression-recognition-9781789451115","title":"Face Analysis Under Uncontrolled Conditions: From","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eFace analysis is essential for a large number of applications such as human-computer interaction or multimedia (e.g. content indexing and retrieval). Although many approaches are under investigation, performance under uncontrolled conditions is still not satisfactory. The variations that impact facial appearance (e.g. pose, expression, illumination, occlusion, motion blur) make it a difficult problem to solve.\u003cbr\u003e\u003cbr\u003eThis book describes the progress towards this goal, from a core building block – landmark detection – to the higher level of micro and macro expression recognition. Specifically, the book addresses the modeling of temporal information to coincide with the dynamic nature of the face. It also includes a benchmark of recent solutions along with details about the acquisition of a dataset for such tasks.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xi\u003cbr\u003e\u003ci\u003eRomain BELMONTE and Benjamin ALLAERT\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1. Facial Landmark Detection 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction to Part 1 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRomain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Facial Landmark Detection 13\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRomain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Facial landmark detection in still images 14\u003c\/p\u003e \u003cp\u003e1.1.1.Generativeapproaches 14\u003c\/p\u003e \u003cp\u003e1.1.2.Discriminative approaches 18\u003c\/p\u003e \u003cp\u003e1.1.3.Deep learningapproaches 24\u003c\/p\u003e \u003cp\u003e1.1.4.Handlingchallenges 34\u003c\/p\u003e \u003cp\u003e1.1.5.Summary 40\u003c\/p\u003e \u003cp\u003e1.2.Extendingfacial landmarkdetectionto videos 41\u003c\/p\u003e \u003cp\u003e1.2.1.Trackingby detection 41\u003c\/p\u003e \u003cp\u003e1.2.2.Box, landmarkand pose tracking 43\u003c\/p\u003e \u003cp\u003e1.2.3.Adaptive approaches 45\u003c\/p\u003e \u003cp\u003e1.2.4. Joint approaches 46\u003c\/p\u003e \u003cp\u003e1.2.5. Temporal constrained approaches 47\u003c\/p\u003e \u003cp\u003e1.2.6.Summary 49\u003c\/p\u003e \u003cp\u003e1.3.Discussion 50\u003c\/p\u003e \u003cp\u003e1.4.References 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Effectiveness of Facial Landmark Detection 67\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRomain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1.Overview 68\u003c\/p\u003e \u003cp\u003e2.2.Datasets and evaluationmetrics 69\u003c\/p\u003e \u003cp\u003e2.2.1. Image and videodatasets 69\u003c\/p\u003e \u003cp\u003e2.2.2. Face preprocessing and data augmentation 73\u003c\/p\u003e \u003cp\u003e2.2.3.Evaluationmetrics 75\u003c\/p\u003e \u003cp\u003e2.2.4.Summary 77\u003c\/p\u003e \u003cp\u003e2.3. Image andvideobenchmarks 77\u003c\/p\u003e \u003cp\u003e2.3.1. Compiled results on 300W 77\u003c\/p\u003e \u003cp\u003e2.3.2. Compiled results on 300VW 79\u003c\/p\u003e \u003cp\u003e2.4.Cross-dataset benchmark 80\u003c\/p\u003e \u003cp\u003e2.4.1.Evaluationprotocol 80\u003c\/p\u003e \u003cp\u003e2.4.2.Comparisonof selected approaches 82\u003c\/p\u003e \u003cp\u003e2.5.Discussion 86\u003c\/p\u003e \u003cp\u003e2.6.References 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Facial Landmark Detection with Spatio-temporal Modeling 93\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRomain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1.Overview 94\u003c\/p\u003e \u003cp\u003e3.2.Spatio-temporalmodelingreview 95\u003c\/p\u003e \u003cp\u003e3.2.1.Hand-craftedapproaches 95\u003c\/p\u003e \u003cp\u003e3.2.2.Deep learningapproaches 97\u003c\/p\u003e \u003cp\u003e3.2.3.Summary 103\u003c\/p\u003e \u003cp\u003e3.3.Architecturedesign 104\u003c\/p\u003e \u003cp\u003e3.3.1. Coordinate regression networks 104\u003c\/p\u003e \u003cp\u003e3.3.2.Heatmapregressionnetworks 106\u003c\/p\u003e \u003cp\u003e3.4.Experiments 107\u003c\/p\u003e \u003cp\u003e3.4.1.Datasets andevaluationprotocols 107\u003c\/p\u003e \u003cp\u003e3.4.2. Implementationdetails 108\u003c\/p\u003e \u003cp\u003e3.4.3.EvaluationonSNaP-2DFe 109\u003c\/p\u003e \u003cp\u003e3.4.4. Evaluation on 300VW 111\u003c\/p\u003e \u003cp\u003e3.4.5.Comparisonwith existingmodels 112\u003c\/p\u003e \u003cp\u003e3.4.6. Qualitative results 112\u003c\/p\u003e \u003cp\u003e3.4.7.Propertiesof the networks 114\u003c\/p\u003e \u003cp\u003e3.5.Design investigations 114\u003c\/p\u003e \u003cp\u003e3.5.1.Encoder-decoder 115\u003c\/p\u003e \u003cp\u003e3.5.2. Complementarity between spatial and temporal information 117\u003c\/p\u003e \u003cp\u003e3.5.3. Complementarity between local and global motion 119\u003c\/p\u003e \u003cp\u003e3.6.Discussion 122\u003c\/p\u003e \u003cp\u003e3.7.References 123\u003c\/p\u003e \u003cp\u003eConclusion to Part 1 133\u003cbr\u003e\u003ci\u003eRomain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2. Facial Expression Analysis 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction to Part 2 149\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBenjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Extraction of Facial Features 157\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBenjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 157\u003c\/p\u003e \u003cp\u003e4.2.Face detection 158\u003c\/p\u003e \u003cp\u003e4.2.1.Point-of-interestdetectionalgorithms 160\u003c\/p\u003e \u003cp\u003e4.2.2.Face alignment approaches 162\u003c\/p\u003e \u003cp\u003e4.2.3.Synthesis 166\u003c\/p\u003e \u003cp\u003e4.3.Face normalization 166\u003c\/p\u003e \u003cp\u003e4.3.1.Dealingwith headpose variations 167\u003c\/p\u003e \u003cp\u003e4.3.2.Dealingwith facial occlusions 170\u003c\/p\u003e \u003cp\u003e4.3.3.Synthesis 172\u003c\/p\u003e \u003cp\u003e4.4.Extractionof visual features 172\u003c\/p\u003e \u003cp\u003e4.4.1.Facial appearancefeatures 172\u003c\/p\u003e \u003cp\u003e4.4.2.Facial geometric features 174\u003c\/p\u003e \u003cp\u003e4.4.3. Facial dynamics features 175\u003c\/p\u003e \u003cp\u003e4.4.4.Facial segmentationmodels 177\u003c\/p\u003e \u003cp\u003e4.4.5.Synthesis 179\u003c\/p\u003e \u003cp\u003e4.5. Learning methods 179\u003c\/p\u003e \u003cp\u003e4.5.1.Classification versus regression 180\u003c\/p\u003e \u003cp\u003e4.5.2.Fusionmodel 182\u003c\/p\u003e \u003cp\u003e4.5.3.Synthesis 184\u003c\/p\u003e \u003cp\u003e4.6.Conclusion 185\u003c\/p\u003e \u003cp\u003e4.7.References 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Facial Expression Modeling 191\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBenjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 191\u003c\/p\u003e \u003cp\u003e5.2.Modelingof the affective state 192\u003c\/p\u003e \u003cp\u003e5.2.1.Categoricalmodeling 192\u003c\/p\u003e \u003cp\u003e5.2.2.Dimensionalmodeling 194\u003c\/p\u003e \u003cp\u003e5.2.3.Synthesis 196\u003c\/p\u003e \u003cp\u003e5.3. The challenges of facial expression recognition 197\u003c\/p\u003e \u003cp\u003e5.3.1. The variation of the intensity of the expressions 197\u003c\/p\u003e \u003cp\u003e5.3.2.Variationof facialmovement 199\u003c\/p\u003e \u003cp\u003e5.3.3.Synthesis 200\u003c\/p\u003e \u003cp\u003e5.4.The learningdatabases 201\u003c\/p\u003e \u003cp\u003e5.4.1. Improvementof learningdata 201\u003c\/p\u003e \u003cp\u003e5.4.2. Comparison of learning databases 203\u003c\/p\u003e \u003cp\u003e5.4.3.Synthesis 205\u003c\/p\u003e \u003cp\u003e5.5. Invariance to facial expression intensities 206\u003c\/p\u003e \u003cp\u003e5.5.1.Macro-expression 206\u003c\/p\u003e \u003cp\u003e5.5.2.Micro-expression 208\u003c\/p\u003e \u003cp\u003e5.5.3.Synthesis 209\u003c\/p\u003e \u003cp\u003e5.6. Invarianceto facialmovements 211\u003c\/p\u003e \u003cp\u003e5.6.1. Pose variations (PV) and large displacements (LD) 211\u003c\/p\u003e \u003cp\u003e5.6.2.Synthesis 214\u003c\/p\u003e \u003cp\u003e5.7.Conclusion 215\u003c\/p\u003e \u003cp\u003e5.8.References 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Facial Motion Characteristics 223\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBenjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 223\u003c\/p\u003e \u003cp\u003e6.2.Characteristics of the facialmovement 225\u003c\/p\u003e \u003cp\u003e6.2.1. Local constraint of magnitude and direction 226\u003c\/p\u003e \u003cp\u003e6.2.2. Local constraint of the motion distribution 228\u003c\/p\u003e \u003cp\u003e6.2.3.Motionpropagationconstraint 230\u003c\/p\u003e \u003cp\u003e6.3.LMP 232\u003c\/p\u003e \u003cp\u003e6.3.1. Local consistency of the movement 233\u003c\/p\u003e \u003cp\u003e6.3.2.Consistencyof local distribution 236\u003c\/p\u003e \u003cp\u003e6.3.3. Coherence in the propagationof themovement 238\u003c\/p\u003e \u003cp\u003e6.4.Conclusion 241\u003c\/p\u003e \u003cp\u003e6.5.References 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Micro- and Macro-Expression Analysis 243\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBenjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 243\u003c\/p\u003e \u003cp\u003e7.2. Definition of a facial segmentation model 244\u003c\/p\u003e \u003cp\u003e7.3.Feature vector construction 247\u003c\/p\u003e \u003cp\u003e7.3.1.Motionfeaturesvector 247\u003c\/p\u003e \u003cp\u003e7.3.2.Geometric featuresvector 248\u003c\/p\u003e \u003cp\u003e7.3.3.Features fusion 249\u003c\/p\u003e \u003cp\u003e7.4. Recognition process 250\u003c\/p\u003e \u003cp\u003e7.5. Evaluation on micro- and macro-expressions 251\u003c\/p\u003e \u003cp\u003e7.5.1.Learningdatabases 252\u003c\/p\u003e \u003cp\u003e7.5.2. Micro-expression recognition 253\u003c\/p\u003e \u003cp\u003e7.5.3. Macro-expressions recognition 255\u003c\/p\u003e \u003cp\u003e7.5.4. Synthesis of experiments on micro- and macro-expressions 258\u003c\/p\u003e \u003cp\u003e7.6. Same expression with different intensities 260\u003c\/p\u003e \u003cp\u003e7.6.1.Data preparation 260\u003c\/p\u003e \u003cp\u003e7.6.2.Fractional time analysis 263\u003c\/p\u003e \u003cp\u003e7.6.3.Analysis on a different time frame 264\u003c\/p\u003e \u003cp\u003e7.6.4. Synthesis of experiments on activation segments 265\u003c\/p\u003e \u003cp\u003e7.7.Conclusion 265\u003c\/p\u003e \u003cp\u003e7.8.References 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Towards Adaptation to Head Pose Variations 271\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBenjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 271\u003c\/p\u003e \u003cp\u003e8.2.Learningdatabase challenges 273\u003c\/p\u003e \u003cp\u003e8.3. Innovative acquisition system (SNaP-2DFe) 274\u003c\/p\u003e \u003cp\u003e8.4. Evaluation of face normalization methods 276\u003c\/p\u003e \u003cp\u003e8.4.1. Does the normalization preserve the facial geometry? 277\u003c\/p\u003e \u003cp\u003e8.4.2. Does normalization preserve facial expressions? 280\u003c\/p\u003e \u003cp\u003e8.5.Conclusion 283\u003c\/p\u003e \u003cp\u003e8.6.References 284\u003c\/p\u003e \u003cp\u003eConclusion to Part 2 287\u003cbr\u003e\u003ci\u003eBenjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Authors 293\u003c\/p\u003e \u003cp\u003eIndex 295\u003c\/p\u003e","brand":"ISTE Ltd","offers":[{"title":"Default Title","offer_id":49412638966103,"sku":"9781789451115","price":112.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781789451115.jpg?v=1730517449"},{"product_id":"we-see-it-all-liberty-and-justice-in-the-age-of-perpetual-surveillance-9781913348694","title":"We See It All: liberty and justice in the age of","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003c\/b\u003e\u003cp\u003e\u003cb\u003eWhat are citizens of a free country willing to tolerate in the name of public safety? \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJon Fasman journeys from the US to London — one of the most heavily surveilled cities on earth — to China and beyond, to expose the legal, political, and moral issues surrounding how the state uses surveillance technology.\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAutomatic licence-plate readers allow police to amass a granular record of where people go, when, and for how long. 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What might happen if all of these technologies are combined and put in the hands of a government with scant regard for its citizens’ civil liberties?\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eThrough on-the-ground reporting and vivid storytelling, Fasman explores one of the most urgent issues of our time.\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e‘[A] deeply reported and sometimes chilling look at mass surveillance technologies in the American justice system … Fasman avoids alarmism while making a strong case for greater public awareness and tighter regulations around these technologies. This illuminating account issues an essential warning about a rising threat to America’s civil liberties.’\u003c\/p\u003e * Publishers Weekly *\u003cbr\u003e\u003cp\u003e‘A cogent critique of the age of ubiquitous surveillance … An urgent examination of police-state intrusions on the privacy of lawful and law-abiding citizens.’\u003c\/p\u003e * Kirkus Reviews *\u003cbr\u003e\u003cp\u003e‘If you want to understand the stakes and the landscape of surveillance in your life — yes, yours right now — \u003ci\u003eWe See It All\u003c\/i\u003e is an outstanding place to start. Fasman walks his readers through a meticulously balanced review of how police, corporations, local businesses, governments, and ordinary people conspire to exchange real privacy for the feeling of safety. An evocative storyteller, Fasman lays out his case that, because government regulation lags impossibly behind technological advances, the only salve for our predicament is collective awareness. And collective action. The writing is sober and sobering. And, though the recent fires of Minneapolis, Atlanta, Portland, and the nation have not centred squarely on surveillance, Fasman argues convincingly that the next ones very well might.’\u003c\/p\u003e -- Phillip Atiba Goff, co-founder and CEO of the Centre for Policing Equity, and professor of African American studies and psychology at Yale University\u003cbr\u003e\u003cp\u003e‘This powerful, engrossing book will challenge your assumptions about persistent surveillance. Jon Fasman makes a clear case for civil liberties and explains how our laws and public safety infrastructure must keep pace with the advancement of technology. It's a must-read for anyone interested in the future and the unintended consequences of artificial intelligence, data, encryption and recognition technology.’\u003c\/p\u003e -- Amy Webb, founder of The Future Today Institute, author of \u003ci\u003eThe Big Nine\u003c\/i\u003e and \u003ci\u003eThe Signals are Talking\u003c\/i\u003e\u003cbr\u003e\u003cp\u003e‘Jon Fasman has given us a stellar account of the use of surveillance technologies by the police. It's comprehensive, even-handed, informative, and fun to read.’\u003c\/p\u003e -- Barry Friedman, Jacob D. Fuchsberg professor at New York University School of Law\u003cbr\u003e\u003cp\u003e‘This lively book is a call to action.’\u003c\/p\u003e -- David Anderson * Literary Review *\u003cbr\u003e\u003cp\u003e‘Attempts to shake us out of our complacency … \u003cem\u003eWe See It All\u003c\/em\u003e is a brutal reminder of the ‘perpetual’ surveillance powers of the police and government.’\u003c\/p\u003e -- Bernard E. 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Two example of pipeline real-time data integration are elaborated: integration of pipeline WebGIS (Geographic Information System) and pipeline SCADA (Supervisory Control and Data Acquisition) via OPC (OLE for Process Control) technology, integration of pipeline network virtual reality system and pipeline SCADA via OPC, JNI (Java Native Interface) and SAI (Scene Access Interface). The pipeline network virtual reality system aims for the pipeline virtual expression, interaction, and 3D visual management. It can be used for pipeline route visual design and plan, immersive pipeline industry training, remote visual supervision and control, etc. The implementation details of the pipeline network virtual reality system, including 3D pipeline and terrain modeling with X3D (Extensible 3D) technology, improving large-scene display performance and speed in the network environment using LOD (Level of Detail) technology, interaction of virtual pipeline scenes, and pipeline 3D visual monitoring, are also introduced. 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Over the fairly recent history of the fields of robotics and computer vision a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field — What is the right algorithm for a particular problem?, and importantly: How can I try it out without spending days coding and debugging it from the original research papers?\u003c\/p\u003e  \u003cp\u003eThe author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used —instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself.\u003c\/p\u003e  \u003cp\u003eThe purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals light and color, camera modelling, image processing, feature extraction and multi-view geometry, and bring it all together in a visual servo system.\u003c\/p\u003e  \u003cp\u003e“An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eOussama Khatib, Stanford\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction.- Part I: Foundations- Representing Position and Orientation.- Part II: Computer Vision.- Light and Color.- Images and Image Processing.- Image Feature Extraction.- Part III: The Geometry of Vision.- Image Formation.- Using Multiple Images.- Index.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415640580439,"sku":"9783030791742","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030791742.jpg?v=1730527618"},{"product_id":"multi-level-bayesian-models-for-environment-perception-9783030836535","title":"Multi-Level Bayesian Models for Environment","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks.  Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models.  Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D\/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Fundamentals. - Bayesian models for Dynamic Scene Analysis.- Multi-layer label fusion models.- Multitemporal data analysis with Marked Point Processes. - Multi-level object population analysis with an EMPP model.- Concluding Remarks.- References.- Index.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415646806359,"sku":"9783030836535","price":79.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030836535.jpg?v=1730527639"},{"product_id":"proceedings-of-the-international-conference-on-intelligent-vision-and-computing-icivc-2021-9783030971953","title":"Proceedings of the International Conference on","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book gathers outstanding research papers presented at the International Conference on Intelligent Vision and Computing (ICIVC 2021), held online during October 03–04, 2021. ICIVC 2021 is organised by Sur University, Oman. The book presents novel contributions in intelligent vision and computing and serves as reference material for beginners and advanced research. The topics covered are intelligent systems, intelligent data analytics and computing, intelligent vision and applications collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal natural language processing.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eHandwritten Bengali Digit Classification using Deep Learning.- IOT Based COVID Patient Health Monitoring System In Quarantine.- Self-attention Convolution for Sparse to Dense Depth Completion.- Using Algorithm in Parametric Design as an Approach to Inspire Nature in Architectural Design.- Docker Container Orchestration Management in Cloud Computing.- Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415668957527,"sku":"9783030971953","price":179.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030971953.jpg?v=1730527722"},{"product_id":"smart-technologies-systems-and-applications-second-international-conference-smarttech-ic-2021-quito-ecuador-december-1-3-2021-revised-selected-papers-9783030991692","title":"Smart Technologies, Systems and Applications:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes refereed proceedings of the Second International Conference on Smart Technologies, Systems and Applications, held in Quito, Ecuador, in December 2021. Due to the COVID-19 pandemic the conference was held in a hybrid format. \u003cbr\u003eThe 29 full papers along with 1 short paper presented were carefully reviewed and selected from 104 submissions. The papers of this volume are organized in topical sections on smart technologies; smart systems; smart trends and applications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eSmart Technologies.- Smart Systems.- Smart Trends and Applications.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415671873879,"sku":"9783030991692","price":71.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030991692.jpg?v=1730527735"},{"product_id":"intelligent-information-processing-xi-12th-ifip-tc-12-international-conference-iip-2022-qingdao-china-may-27-30-2022-proceedings-9783031059124","title":"Intelligent Information Processing XI: 12th IFIP","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book constitutes the refereed proceedings of the 12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022, held in Qingdao, China, in July 2022.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003eThe 37 full papers and 6 short papers presented were carefully reviewed and selected from 57 submissions. They are organized in topical sections on Machine Learning, Data Mining, Multiagent Systems, Social Computing, Blockchain Technology, Game Theory and Emotion, Pattern Recognition, Image Processing and Applications.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eMachine Learning.- \u003c\/b\u003eAn AdaBoost Based- Deep Stochastic Configuration Network.- Comparative Study of Chaos-embedded Particle Swarm Optimization.- A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification.- Inductive Light Graph Convolution Network for Text Classification based on Word-Label Graph.- Sparse Subspace Clustering Based on Adaptive Parameter Training.- A Hybrid Multi-objective Optimization Algorithm with Improved Neighborhood Rough Sets for Feature Selection.- Augmenting Convolution Neural Networks By Utilizing Attention Mechanism for Knowledge Tracing.- \u003cb\u003eData Mining.- \u003c\/b\u003eInteractive Mining of User-Preferred Co-Location Patterns Based on SVM.- Classification between Rumors and Explanations of Rumors based on Common and Difference Subsequences of Sentences.- Double-Channel Multi-layer Information Fusion for Text Matching.- Augmenting Context Representation with Triggers Knowledge for Relation Extraction.- Does Large Pretrained Dataset always help? On the Effect of Dataset Size on Big Transfer Model.- Using Multi-level Attention based on Concept Embedding Enrichen Short Text to Classification.- \u003cb\u003eMultiagent Systems.- \u003c\/b\u003ePre-loaded Deep-Q Learning.- Resource Scheduling for Human-Machine Collaboration in Multiagent Systems.- Social Computing.- Automatic Generation and Analysis of Role Relation Network from Emergency Plans.- Information Tracking Extraction for Emergency Scenario Response.- Neighborhood Network for Aspect-based Sentiment Analysis.- A Hybrid Parallel Algorithm with Multiple Improved Strategies.- \u003cb\u003eBlockchain Technology.- \u003c\/b\u003eResearch on Blockchain Privacy Protection Mechanism in Financial Transaction Services based on Zero-knowledge Proof and Federal Learning.- A Distributed Supply Chain Architecture Based on Blockchain Technology.- \u003cb\u003eGame Theory and Emotion.- \u003c\/b\u003eA Game-Theoretic Analysis of Impulse Purchase.- A Self-supervised Strategy for the Robustness of VQA Models.- Employing Contrastive Strategies for Multi-label Textual Emotion Recognition.- \u003cb\u003ePattern Recognition.- \u003c\/b\u003eFault Localization Based on Deep Neural Network and Execution Slicing.- Defect Detection and Classification of Strip Steel based on Improved VIT Model.- ROSES: A novel semi-supervised feature selector.- Improving speech emotion recognition by fusing pre-trained and acoustic features using Transformer and BiLSTM.- A Pear Leaf Diseases Image Recognition Model Based on Capsule Network.- Software Defect Prediction Method Based on Cost-Sensitive Random Forest.- Fault Diagnosis of Sewage Treatment Equipment Based on Feature Selection.- Attention Adaptive Chinese Named Entity Recognition Based on Vocabulary Enhancement.- \u003cb\u003eImage Processing.- \u003c\/b\u003eA HEp-2 Cell Image Classification Model Based on Deep Residual Shrinkage Network Combined with Dilated Convolution.- A Method on Online Learning Video Recommendation Method Based on Knowledge Graph.- Data Transformation for Super-Resolution on Ocean Remote Sensing Images.- A Novel RGBD Image Superpixel Segmentation Intergrated Depth Map Quality.- Super-Resolution of Defocus Thread Image Based on Cycle Generative Adversarial Networks.- Multi-instance Learning for Semantic Image Analysis.- High-resolution Remote Sensing Image Semantic Segmentation Method Based on Improved Encoder-Decoder Convolutional Neural Network.- \u003cb\u003eApplications.- \u003c\/b\u003eA Method for AGV Double-cycling Scheduling at Automated Container Terminals.- Predicting Student Performance In Online Learning Using A Highly Efficient Gradient Boosting Decision Tree.- Adapting on Road Traffic-oriented Controlled Optimization of Phases to Heterogeneous Intersections.- A Method of Garbage Quantity Prediction based on Population Change.\u003ci\u003e\u003c\/i\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415677542743,"sku":"9783031059124","price":104.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031059124.jpg?v=1730527754"},{"product_id":"image-analysis-and-processing-iciap-2022-21st-international-conference-lecce-italy-may-23-27-2022-proceedings-part-i-9783031064265","title":"Image Analysis and Processing – ICIAP 2022: 21st","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy,\u003cp\u003eThe 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc. \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eBrave New Ideas.- Biomedical and Assistive Technology.- Multimedia.- Deep Learning.- Image Processing for Cultural Heritage.- Robot Vision.\u003cbr\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415677837655,"sku":"9783031064265","price":89.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031064265.jpg?v=1730527755"},{"product_id":"third-international-conference-on-image-processing-and-capsule-networks-icipcn-2022-9783031124129","title":"Third International Conference on Image","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides a collection of the state-of-the-art research attempts to tackle the challenges in image and signal processing from various novel and potential research perspectives. The book investigates feature extraction techniques, image enhancement methods, reconstruction models, object detection methods, recommendation models, deep and temporal feature analysis, intelligent decision support systems, and autonomous image detection models. In addition to this, the book also looks into the potential opportunities to monitor and control the global pandemic situations.\u003c\/p\u003e  \u003cp\u003eImage processing technology has progressed significantly in recent years, and it has been commercialized worldwide to provide superior performance with enhanced computer\/machine vision, video processing, and pattern recognition capabilities. Meanwhile, machine learning systems like CNN and CapsNet get popular to provide better model hierarchical relationships and attempts to more closely mimic biological neural organization. As machine learning systems prosper, image processing and machine learning techniques will be tightly intertwined and continuously promote each other in real-world settings.\u003cbr\u003e Adopting this trend, however, the image processing researchers are faced with few image reconstruction, analysis, and segmentation challenges. On the application side, the orientation of the image features and noise removal has become a huge burden.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415685669207,"sku":"9783031124129","price":189.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031124129.jpg?v=1730527786"},{"product_id":"towards-autonomous-robotic-systems-23rd-annual-conference-taros-2022-culham-uk-september-7-9-2022-proceedings-9783031159077","title":"Towards Autonomous Robotic Systems: 23rd Annual","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe volume LNAI 13546 constitutes the refereed proceedings of the 23rd Annual Conference Towards Autonomous Robotic Systems, TAROS 2022, held in Culham, UK, in September 2022.\u003c\/p\u003e  \u003cp\u003eThe 14 full papers and 10 short papers were carefully reviewed and selected from 38 submissions. Organized in the topical sections \"Algorithms\" and \"Systems\", they discuss significant findings and advances in the following areas: Robotic Grippers and Manipulation; Soft Robotics, Sensing and Mobile Robots; Robotic Learning, Mapping and Planning; Robotic Systems and Applications.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eA distributed approach to haptic simulation.- A Novel Two-Hand-Inspired Hybrid Robotic End-Effector Fabricated Using 3D Printing.- Investigating the relationship between posture and safety in teleoperational tasks: A pilot study in improved operational safety through enhanced human-machine interaction.- Design and Analysis of an End Effector Using the Fin Ray Structure for Integrated Limb Mechanisms.- Trigger-Assisted Ambidextrous Control Framework for Teleoperation of Two Legged Manipulators.- Teleoperating a Legged Manipulator through Whole-Body Control.- In-silico Design and Computational Modelling of Electroactive Polymer based Soft Robotics.- Exploration of Underwater Storage Facilities with Swarm of Micro-Surface Robots.- Characterization of an Inflatable Soft Actuator and Tissue Interaction for In Vitro Mechanical Stimulation of Tissue.- EMap: Real-time terrain estimation.- Design and Preliminary In-Classroom Evaluation\\\\of a Low-Cost Educational Mobile Robot.- Internal State-based Risk Assessment for Robots in Hazardous Environment.- Investigating Scene Visibility Estimation within ORB-SLAM3.- Tactile and Proprioceptive Online Learning in Robotic Contour Following.- Learning cooperative behaviours in adversarial multi-agent systems.- Task Independent Safety Assessment for Reinforcement Learning.- Sensing Anomalies as Potential Hazards: Datasets and Benchmarks.- Integration and robustness analysis of the Buzz swarm programming language with the Pi-puck robot platform.- Implementing and assessing a remote teleoperation setup with a Digital Twin using cloud networking.- Agent-Based Simulation of Multi-Robot Soil Compaction Mapping.- A-EMS: An Adaptive Emergency Management System for Autonomous Agents in Unforeseen Situations.- Towards Scalable Multi-Robot Systems by Partitioning the Task Domain.- Effectiveness of brush operational parameters for robotic debris removal.- Automatic, Vision-Based Tool Changing Solution for Dexterous Teleoperation Robots in a Nuclear Glovebox","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415690092887,"sku":"9783031159077","price":53.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031159077.jpg?v=1730527800"},{"product_id":"domain-adaptation-and-representation-transfer-4th-miccai-workshop-dart-2022-held-in-conjunction-with-miccai-2022-singapore-september-22-2022-proceedings-9783031168512","title":"Domain Adaptation and Representation Transfer:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the refereed proceedings of the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with MICCAI 2022, in September 2022. \u003cbr\u003eDART 2022 accepted 13 papers from the 25 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)\/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.\u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eDetecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification.- Benchmarking Transformers for Medical Image Classification.- Supervised domain adaptation using gradients transfer for improved medical image analysis.- Stain-AgLr: Stain Agnostic Learning for Computational Histopathology using Domain Consistency and Stain Regeneration Loss.- MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation.- Unsupervised site adaptation by intra-site variability alignment.- Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining.- POPAR: Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis.- Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging.- Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images.- Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts.- Adaptive Optimization with Fewer Epochs Improves Across-Scanner Generalization of U-Net based Medical Image Segmentation.- CateNorm: Categorical Normalization for Robust Medical Image Segmentation.\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415692124503,"sku":"9783031168512","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031168512.jpg?v=1730527805"}],"url":"https:\/\/bookcurl.com\/collections\/computer-vision.oembed?page=7","provider":"Book Curl","version":"1.0","type":"link"}