Computer vision Books
Springer Computer Vision ACCV 2024 Workshops
£999.99
Springer Computer Vision ACCV 2024 Workshops
£999.99
£999.99
Springer Emotional Intelligence
£116.99
£999.99
Springer Deep Learning for 3D Point Clouds
Book SynopsisChapter 1. Introduction to 3D Point Clouds: Datasets and Perception.- Chapter 2. Learning Basics for 3D Point Clouds.- Chapter 3. Deep Learning-based Point Cloud Enhancement I.- Chapter 4. Deep Learning-based Point Cloud Enhancement II.- Chapter 5. Deep Learning-based Point Cloud Analysis I.- Chapter 6. Deep Learning-based Point Cloud Analysis II.- Chapter 7. Point Cloud Pre-trained Models and Large Models.- Chapter 8. Point Cloud-Language Multi-modal Learning.- Chapter 9. Open Source Projects for 3D Point Clouds.- Chapter 10. Typical Engineering Applications of 3D Point Clouds.- Chapter 11. FutureWork on Deep Learning-based Point Cloud Technologies.
£999.99
Springer Computer Vision Metrics
Book SynopsisChapter 1. 2D/3D Image Capture and Representation.- Chapter 2. Image Pre-Processing Taxonomy, Colorimetry.- Chapter 3. Global and Regional Feature Descriptors.- Chapter 4. Local Feature Descriptors.- Chapter 5. Feature Descriptor Attribute Taxonomy.- Chapter 6. Feature Detector and Descriptor Survey.- Chapter 7. Ground Truth Data Topics, Benchmarks, Analysis.- Chapter 8. Vision Pipelines and HW/SW Optimizations.- Chapter 9. Feature Learning Taxonomy and Neuroscience Background.-Chapter 10. Feature Learning and Deep Learning Survey.- Chapter 11. Attention, Transformers, Hybrids, DDN's.- Chapter 12. Applied And Future Visual Computing Topics.
£999.99
Independently Published AI for Inspection
£27.20
Independently Published TechDriven CounterNarcoterrorism
£31.75
Amazon Digital Services LLC - Kdp Gemini 3.5 Pro
£15.03
Independently Published MLOps Fundamentals
£14.72
Independently Published Advanced Computer Vision with OpenCV 4
£63.34
Amazon Digital Services LLC - Kdp Smart Dozen
£11.54
Amazon Digital Services LLC - Kdp LLM Transformers
Book Synopsis
£12.68
Birkhauser Co-Corporeality of Humans, Machines, & Microbes
Book SynopsisThe theory of Co-Corporeality is based on a conception of the built environment as a biological entity that opens up a space for coexistence and interaction between humans and microbial life. Based on design-led research, this book explores how we can develop environments for a multispecies world. It focuses on the agency of both human and nonhuman actors. New sensor tools enable observation of and interaction between these different actors. Co-Corporeality links microbiology to material science, artificial intelligence, and architecture. The focus is on how microbial activity can create new protoarchitectural materials, how living systems can be integrated into architecture and cooperate along different time scales.
£31.05
John Wiley & Sons Inc Image Segmentation Principles Techniques and
Book SynopsisImage 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.Table of ContentsPreface About the Authors List of Abbreviations Part One: Principle 1 Introduction to Image Segmentation 2 Principles of Clustering 3 Principles of Mathematical Morphology 4 Principles of Neural Network Part Two: Methods 5 Fast and Robust Image Segmentation Using Clustering 6 Fast Image Segmentation Using Watershed Transform 7 Superpixel-based Fast Image Segmentation Part Three: Application 8 Image Segmentation for Traffic Scene Analysis 9 Image Segmentation for Medical Analysis 10 Image Segmentation for Remote Sensing Analysis 11 Image Segmentation for Material Analysis
£99.00
Scribe Publications We See It All: liberty and justice in the age of
Book SynopsisWhat are citizens of a free country willing to tolerate in the name of public safety? Jon 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. Automatic licence-plate readers allow police to amass a granular record of where people go, when, and for how long. Drones give the state eyes — and possibly weapons — in the skies. Algorithms purport to predict where and when crime will occur, and how big a risk a suspect has of reoffending. Specially designed tools can crack a device’s encryption keys, rending all privacy protections useless. And facial recognition technology poses perhaps a more dire and lasting threat than any other form of surveillance. Jon Fasman examines how these technologies help police do their jobs, and what their use means for our privacy rights and civil liberties, exploring vital questions, such as: Should we expect to be tracked and filmed whenever we leave our homes? Should the state have access to all of the data we generate? Should private companies? 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? Through on-the-ground reporting and vivid storytelling, Fasman explores one of the most urgent issues of our time.Trade Review‘[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.’ * Publishers Weekly *‘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.’ * Kirkus Reviews *‘If you want to understand the stakes and the landscape of surveillance in your life — yes, yours right now — We See It All 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.’ -- Phillip Atiba Goff, co-founder and CEO of the Centre for Policing Equity, and professor of African American studies and psychology at Yale University‘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.’ -- Amy Webb, founder of The Future Today Institute, author of The Big Nine and The Signals are Talking‘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.’ -- Barry Friedman, Jacob D. Fuchsberg professor at New York University School of Law‘This lively book is a call to action.’ -- David Anderson * Literary Review *‘Attempts to shake us out of our complacency … We See It All is a brutal reminder of the ‘perpetual’ surveillance powers of the police and government.’ -- Bernard E. Harcourt * TLS *Praise for The Unpossessed City: ‘Bestseller Fasman … takes a compassionate look at the hard truths of modern-day Russia in his absorbing second novel … The bio-thriller aspect of the plot provides a loose frame for Fasman's real concerns … and, more importantly, the trials and tribulations of the new Russia itself.’ * Publishers Weekly *Praise for The Geographer's Library: ‘A brainy noir … [A] winningly cryptic tale … [A] cabinet of wonders written by a novelist whose surname and sensibility fit comfortably on the shelf between Umberto Eco and John Fowles.’ * Los Angeles Times *Praise for The Geographer's Library: ‘One of the year's most literate and absorbing entertainments.’ -- Kirkus Reviews
£15.29
Springer International Publishing AG Computer Vision – ACCV 2022: 16th Asian
Book SynopsisThe 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022. The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; optimization methods; Part II: applications of computer vision, vision for X; computational photography, sensing, and display; Part III: low-level vision, image processing; Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics; Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis; Part VI: biomedical image analysis; deep learning for computer vision; Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.
£80.74
Springer Computational Visual Media
Book SynopsisImage and Video AnalysisDepthFisheye: Efficient Fine-Tuning of Depth Estimation Models for Fisheye Cameras.- DIMATrack: Dimension Aware Data Association for Multi-Object Tracking.- Efficient Transformer Network for Visible and Ultraviolet Object Tracking.- LightGR-Transformer: Light Grouped Residual Transformer for Multispectral Object Detection.- ADMMOA: Attribute-Driven Multimodal Optimization for Face Recognition Adversarial Attacks.- Training-Free Language-Guided Video Summarization via Multi-Grained Saliency Scoring.- Multimodal LearningReinforced Label Denoising for Weakly-Supervised Audio-Visual Video Parsing.- Bridging the Modality Gap: Advancing Multimodal Human Pose Estimation with Modality-Adaptive Pose Estimator and Novel Benchmark Datasets.- Momentum-Based Uni-Modal Soft-Label Alignment and Multi-Modal Latent Projection Networks for Optimizing Image-Text Retrieval.- Multi-Granularity and Multi-Modal Prompt Learning for Person Re-Identification.- Local and Global Feature Cross-attention Multimodal Place Recognition.- IML-CMM - A Multimodal Sentiment Analysis Framework Integrating Intra-Modal Learning and Cross-Modal Mixup Enhancement.- Geometrical ProcessingMCFG with GUMAP: A Simple and Effective Clustering Framework on Grassmann Manifold.- Joint UMAP for Visualization of Time-Dependent Data.- Unsupervised Domain Adaptation on Point Cloud Classification via Imposing Structural Manifolds into Representation Space.- ApplicationsLearning Adaptive Basis Fonts to Fuse Content Features for Few-shot Font Generation.- TaiCrowd: A High-Performance Simulation Framework for Massive Crowd.-Feature Disentanglement and Fusion Model for Multi-Source Domain Adaptation with Domain-Specific Features.- A Trademark Retrieval Method Based on Self-Supervised Learning.- Weaken Noisy Feature: Boosting Semi-Supervised Learning by Noise Estimation.- Multi-Dimension Full Scene Integrated Visual Emotion Analysis Network.- Gap-KD: Bridging the Significant Capacity Gap Between Teacher and Student Model.
£61.74
Springer Generative Adversarial Network
Book Synopsis"Chapter 1 Generative Model".- "Chapter 2 Objective Function Optimization".- "Chapter 3 Training Techniques".- "Chapter 4 Evaluation Methods and Visualization".- "Chapter 5 Image Generation".- "Chapter 6 Image Translation".- "Chapter 7 Face Image Editing".- "Chapter 8 Image Quality Enhancement".- "Chapter 9 Generation of 3D pictures and videos".- "Chapter 10 General Image Editing".- "Chapter 11 Adversarial Attack".- "Chapter 12 Speech Signal Processing".
£58.49
O'Reilly Media Explainable AI for Practitioners
Book SynopsisExplainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability.
£47.99
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£80.74
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£80.74
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£80.74
Springer International Publishing AG Web and Big Data: 6th International Joint
Book SynopsisThis three-volume set, LNCS 13421, 13422 and 13423, constitutes the thoroughly refereed proceedings of the 6th International Joint Conference, APWeb-WAIM 2022, held in Nanjing, China, in August 2022.The 75 full papers presented together with 45 short papers, and 5 demonstration papers were carefully reviewed and selected from 297 submissions. The papers are organized around the following topics: Big Data Analytic and Management, Advanced database and web applications, Cloud Computing and Crowdsourcing, Data Mining, Graph Data and Social Networks, Information Extraction and Retrieval, Knowledge Graph, Machine Learning, Query processing and optimization, Recommender Systems, Security, privacy, and trust and Blockchain data management and applications, and Spatial and multi-media data.Table of ContentsResearch tracks. Big Data Analytic and Management.- Advanced database and web applications.- Cloud Computing and Crowdsourcing.- Data Mining.- Graph Data and Social Networks.- Information Extraction and Retrieval.- Knowledge Graph.- Machine Learning.- Query processing and optimization.- Recommender Systems.- Security, privacy, and trust& Blockchain data management and applications.- Spatial and multi-media data.- Demo papers.
£66.49
Springer International Publishing AG Pattern Recognition, Computer Vision, and Image
Book SynopsisThis 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26th International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presented in these 4 volumes were carefully reviewed and selected from numerous submissions. ICPR workshops covered domains related to pattern recognition, artificial intelligence, computer vision, image and sound analysis. Workshops’ contributions reflected the most recent applications related to healthcare, biometrics, ethics, multimodality, cultural heritage, imagery, affective computing, etc.Table of ContentsTowards a Complete Analysis of People: From Face and Body to Clothes (T-CAP).- 12th International Workshop on Human Behavior Understanding (HBU).- Theories, Applications, and Cross Modality for Self-Supervised Learning Models (SSL).- Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS 2022).- Fairness in Biometric Systems (FAIRBIO).- 2nd International Workshop on Artificial Intelligence for Healthcare Applications (AIHA 2022).- Multimodal Data for Mental Disorder Recognition (MDMR).- MANPU 2022 : The 5th International Workshop on coMics ANalysis, Processing and Understanding.- Image Analysis for Forest Environmental Monitoring (FOREST).- MultiMedia FORensics in the WILD (MMFORWILD 2022).- Image Mining: Theory and Applications (IMTA-VIII).- International Workshop on Pattern Recognition in Healthcare Analytics (PRHA 2022).- International Workshop on Industrial Machine Learning (IML).- 3rd International Workshop on Pattern Recognition for Cultural Heritage (PatReCH 2022).- 2nd Workshop on Explainable and Ethical AI (XAIE 2022).- 12th Workshop on Pattern Recognition in Remote Sensing (PRRS).- Understanding and Mitigating Demographic Bias in Biometric Systems (UMDBB).- Artificial Intelligence for Multimedia Forensics and Disinformation Detection (AI4MFDD).- 3rd Workshop on Applied Multimodal Affect Recognition (AMAR).
£113.99
Springer International Publishing AG Advanced Concepts for Intelligent Vision Systems:
Book SynopsisThis book constitutes the proceedings of the 21st International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2023, held in Kumamoto, Japan, during August 2023. The 31 papers presented in this volume were carefully reviewed and selected from a total of 48 submissions. They were organized in topical sections named: Computer Vision, Affective Computing and Human Interactions, Managing the Biodiversity, Robotics and Drones, Machine Learning.Table of ContentsA hybrid quantum-classical segment-based Stereo Matching algorithm.- Continuous Exposure for Extreme Low-Light Imaging.- Semi-supervised Classification and Segmentation of Forest Fire using Autoencoders.- Descriptive and coherent paragraph generation for image paragraph captioning using vision transformer and post-processing.- Pyramid Swin Transformer for Multi-Task: Expanding to more computer vision tasks.- Person activity classification from an aerial sensor based on a multi-level deep features.- Person Quick-Search Approach based on a Facial Semantic Attributes Description.- Age-Invariant Face Recognition using Face Feature Vectors and Embedded Prototype Subspace Classifiers.- BENet: A lightweight bottom-up framework for context-aware emotion recognition.- Yolopoint: Joint Keypoint and Object Detection.- Less-than-one shot 3d segmentation hijacking a pre-trained space-time memory network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- A Single Image Neuro-Geometric Depth Estimation.- Wave-shaping Neural Activation for Improved 3D Model Reconstruction from Sparse Point Clouds.- A Deep Learning Approach to Segment High-Content Images of the E.coli Bacteria.- Multimodal Emotion Recognition System Through Three Different Channels (MER-3C).- Multi-Modal Obstacle Avoidance in USVs via Anomaly Detection and Cascaded Datasets.- A Contrario Mosaic Analysis for Image Forensics.- IRIS SEGMENTATION TECHNIQUE USING IRIS-UNet METHOD.- Image Acquisition by Image Retrieval with Color Aesthetics.- Improved Obstructed Facial Feature Reconstruction for Emotion Recognition with Minimal Change CycleGANs.- Quality assessment for high dynamic range stereoscopic omnidirectional image system.- Genetic Programming with Convolutional Operators for Albatross Nest Detection from Satellite Imaging.- Reinforcement Learning for truck Eco-driving: a serious game as driving assistance system.- Underwater mussel segmentation using smoothed shape descriptors with random forest.- A 2D Cortical Flat Map Space for Computationally Efficient Mammalian Brain simulation.- Construction of a novel data set for pedestrian tree species detection using google street view data.- Texture-based Data Augmentation for Small Datasets.- Multimodal Representations for Teacher-Guided Compositional Visual Reasoning.- Enhanced Color QR Codes with Resilient Error Correction for Dirt-Prone Surfaces.
£56.99
Springer International Publishing AG Advances in Visual Computing: 18th International
Book SynopsisThis volume LNCS 14361 and 14362 constitutes the refereed proceedings of the, 16th International Symposium, ISVC 2023, in October 2023, held at Lake Tahoe, NV, USA.The 42 full papers and 13 poster papers were carefully reviewed and selected from 120 submissions. A total of 25 papers were also accepted for oral presentation in special tracks from 34 submissions. The following topical sections followed as:Part 1: ST: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management; Visualization; Video Analysis and Event Recognition; ST: Innovations in Computer Vision & Machine Learning for Critical & Civil Infrastructures; ST: Generalization in Visual Machine Learning; Computer Graphics; Medical Image Analysis; Biometrics; Autonomous Anomaly Detection in Images; ST: Artificial Intelligence in Aerial and Orbital Imagery; ST: Data Gathering, Curation, and Generation for Computer Vision and Robotics in Precision Agriculture.Part 2: Virtual Reality; Segmentation; Applications; Object Detection and Recognition; Deep Learning; Poster.Table of ContentsST: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management.- Hybrid Region and Pixel-Level Adaptive Loss for Mass Segmentation on Whole Mammography Images.- Deep Learning Based GABA Edited-MRS Signal Reconstruction.- Investigating the Impact of Attention on Mammogram Classification.- ReFit:A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical Images.- A Data-centric Approach for Pectoral Muscle Deep Learning Segmentation Enhancements in Mammography Images.- Visualization.- Visualizing Multimodal Time Series at Scale.- Hybrid Tree Visualizations for Analysis of Gerrymandering.- ArcheryVis: A Tool for Analyzing and Visualizing Archery Performance Data.- Spiro: Order-preserving Visualization in High Performance Computing Monitoring.- From Faces To Volumes - Measuring Volumetric Asymmetry in 3D Facial Palsy Scans.- Video Analysis and Event Recognition.- Comparison of Autoencoder Models for Unsupervised Representation Learning of Skeleton Sequences.- Local and Global Context Reasoning for Spatio-Temporal Action Localization.- Zero-Shot Video Moment Retrieval using BLIP-based Models.- Self-Supervised Representation Learning for Fine Grained Human Hand Action Recognition in Industrial Assembly Lines.- ST: Innovations in Computer Vision & Machine Learning for Critical & Civil Infrastructures.- Pretext Tasks in Bridge Defect Segmentation within a ViT-Adapter Framework.- A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation.- Efficient Resource Provisioning in Critical Infrastructures based on Multi-Agent Rollout enabled by Deep Q-Learning.- Video-Based Recognition of Aquatic Invasive Species Larvae Using Attention-LSTM Transformer.- ST: Generalization in Visual Machine Learning.- Latent Space Navigation for Face Privacy: A Case Study on the MNIST Dataset.- Domain Generalization for Foreground Segmentation Using Federated Learning.- Probabilistic Local Equivalence Certification for Robustness Evaluation.- Challenges of Depth Estimation for Transparent Objects.- Volumetric Body Composition through Cross-Domain Consistency Training for Unsupervised Domain Adaptation.- Computer Graphics.- Water Animation Using Coupled SPH and Wave Equation.- Neural Rendering into Unity.- Virtual Home Staging: Inverse Rendering and Editing an Indoor Panorama under Natural Illumination.- SwarmCurves: Evolutionary Curve Reconstruction.- Medical Image Analysis.- Brain Cortical Surface Registration with Anatomical Atlas Constraints.- When System Model meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging.- 3D Reconstruction from 2D Cerebral Angiograms as a Volumetric Denoising Problem.- An Integrated Shape-Texture Descriptor for Modeling Whole-Organism Phenotypes in Drug Screening.- Enhancing Image Reconstruction via Phase-Constrained Data in an Iterative Process.- Biometrics.- I Got Your Emotion: Emotion Preserving Face De-identification Using Injection-based Generative Adversarial Networks.- DoppelVer: A Benchmark for Face Verification.- Two-stage Face Detection and Anti-spoofing.- Autonomous Anomaly Detection in Images.- Driver Anomaly Detection Using Skeleton Images.- Driver Anomaly Detection Using Skeleton Images.- Latent Diffusion based Multi-class Anomaly Detection.- ST: Artificial Intelligence in Aerial and Orbital Imagery.- Investigating the impact of a low-rank tensor-based approach on deforestation imagery.- Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning A Case Study on Object Tracking Tasks.- Autonomous Navigation Via A Cascading CNN Framework Leveraging Synthetic Terrain Images.- ST: Data Gathering, Curation, and Generation for Computer Vision and Robotics in Precision Agriculture.- Synthetically labeled images for maize plant detection in UAS images.- An open source simulation toolbox for annotation of images and point clouds in agricultural scenarios.- Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms.- Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches.- Deep Learning for Super Resolution of Sugarcane Crop Line Imagery from Unmanned Aerial Vehicles.
£75.99
Springer International Publishing AG MultiMedia Modeling: 30th International
Book SynopsisThis book constitutes the refereed proceedings of the 30th International Conference on MultiMedia Modeling, MMM 2024, held in Amsterdam, The Netherlands, during January 29–February 2, 2024.The 112 full papers included in this volume were carefully reviewed and selected from 297 submissions. The MMM conference were organized in topics related to multimedia modelling, particularly: audio, image, video processing, coding and compression; multimodal analysis for retrieval applications, and multimedia fusion methods.Table of ContentsSelf-distillation Enhanced Vertical Wavelet Spatial Attention for Person Re-identification.- High Capacity Reversible Data Hiding in Encrypted Images Based onPixel Value Preprocessing and Block Classification.- HPattack: An Effective Adversarial Attack for Human Parsing.- Dynamic-Static Graph Convolutional Network for Video-Based Facial Expression Recognition.- Hierarchical Supervised Contrastive Learning for Multimodal Sentiment Analysis.- Semantic Importance-Based Deep Image Compression Using A Generative Approach.- Drive-CLIP: Cross-modal Contrastive Safety-Critical Driving Scenario Representation Learning and Zero-shot Driving Risk Analysis.- MRHF: Multi-stage Retrieval and Hierarchical Fusion for Textbook Question Answering.- Multi-scale Decomposition Dehazing with Polarimetric Vision.- CLF-Net: A Few-shot Cross-Language Font Generation Method.- Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation.- Audio-Visual Segmentation By Leveraging Multi-Scaled Features Learning.- Multi-head Hashing with Orthogonal Decomposition for Cross-modal Retrieval.- Fusion Boundary and Gradient Enhancement Network for Camouflage Object Detection.- Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas.- SM-GAN: Single-stage and Multi-object Text Guided Image Editing.- MAVAR-SE: Multi-scale Audio-Visual Association Representation Network for End-to-end Speaker Extraction.- NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide Images.- Improving Small License Plate Detection with Bidirectional Vehicle-plate Relation.- A Purified Stacking Ensemble Framework for Cytology Classification.- SEAS-Net: Segment Exchange Augmentation for Semi-Supervised Brain Tumor Segmentation.- Super-Resolution-Assisted Feature Refined Extraction for Small Objects in Remote Sensing Images.- Lightweight Image Captioning Model Based on Knowledge Distillation.- Irregular License Plate Recognition via Global Information Integration.- TNT-Net: Point Cloud Completion by Transformer in Transformer.- Fourier Transformer for Joint Super-Resolution and Reconstruction ofMr Image.- MVD-NeRF: Resolving Shape-Radiance Ambiguity via Mitigating View Dependency.- DPM-Det: Diffusion Model Object Detection Based on DPM-Solver++Guided Sampling.- CT-MVSNet: Efficient Multi-View Stereo with Cross-scale Transformer.- A Coarse and Fine Grained Masking Approach for Video-groundedDialogue.- Deep self-supervised subspace clustering with triple loss.- LigCDnet:Remote Sensing Image Cloud Detection Based on Lightweight Framework.- Gait Recognition Based on Temporal Gait Information Enhancing.- Learning Complementary Instance Representation with Parallel Adaptive Graph-Based Network for Action Detection.- CESegNet:Context-Enhancement Semantic Segmentation NetworkBased on Transformer.- MoCap-Video Data Retrieval with Deep Cross-Modal Learning.- LRATNet: Local-Relationship-Aware Transformer Network for TableStructure Recognition.
£66.49
Springer Ethics and Fairness in Medical Imaging
Book SynopsisFAIMI: Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods.- Dataset Distribution Impacts Model Fairness: Single vs Multi-Task Learning.- AI Fairness in Medical Imaging: Controlling for Disease Severity.- Fair and Private CT Contrast Agent Detection.- Mitigating Overdiagnosis Bias in CNN-Based Alzheimer's Disease Diagnosis for the Elderly.- Fair AI Outcomes Without Sacrificing Group Gains .- All you need is a guiding hand: mitigating shortcut bias in deep learning models for medical imaging.- Exploring Fairness in State-of-the-Art Pulmonary Nodule Detection Algorithms.- Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation.- BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning.- Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data.- Do sites benefit equally from distributed learning in medical image analysis.-
£42.74
Springer Shape in Medical Imaging
Book SynopsisWeakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images.- PSGMM: Pulmonary Segment Segmentation Based on Gaussian Mixture Model.- Deformable vertebra 3D/2D registration from biplanar X-rays using particle-based shape modelling.- Deep Combined Computing of Vascular Images with Tubular Shape-Guided Convolution.- Implicitly Explicit: Segmenting Vertebrae with Deep Implicit Statistical Shape Models.- 3D Body Twin: Improving Human Gait Visualizations Using Personalized Avatars.- Robust Curve Detection in Volumetric Medical Imaging via Attraction Field.- A Critical Comparison Between Template-Based and Architecture-Reused Deep Learning Methods for Generic 3D Landmarking of Anatomical Structures.- Adaptive Bi-ventricle Surface Reconstruction from Cardiovascular Imaging.- Application of Deep Statistical Shape Modeling for Analysis of Obstructive Sleep Apnea from MRI Data.- Leveraging Expert Knowledge for Real-time Online Adaptation of Intraoperative Liver Registration.- MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images.- LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation.- Enhancing Multimodal Image-Based Classification of Alzheimer's Disease with Surface Information.- Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representations.- Towards Point Cloud-Based Medical Image Registration for Dynamic 4D-CT Imaging.
£47.49
Springer Pattern Recognition
Book SynopsisOptimizing Personalized Robot Actions with Ranking of Trajectories.- Synthesizing operationally safe controllers for human-in-the-loop human-in-the-plant hybrid close loop systems.- Multi-Frequency Fine-Grained Matching for Audio-Visual Segmentation.- Confidence-Guided Feature Alignment for Cloth-Changing Person Re-identification.- Fair Latent Representation Learning with Adaptive Reweighing.- FineFACE: Fair Facial Attribute Classification Leveraging Fine-grained Features.- One-factor Cancelable Biometric Template Protection Scheme for Real-valued Features.- Multi-Teacher Invariance Distillation for Domain-Generalized Action Recognition.- ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recognition.- Spatio-Temporal Domain-Aware Network for Skeleton-based Action Representation Learning.- Project and Pool: An Action Localization network for localizing actions in Untrimmed Videos.- Multi-Teacher Importance Preserving Knowledge Distillation f
£104.49
Springer Shape in Medical Imaging
Book Synopsis.- Anisotropic Fourier Features for Positional Encoding in Medical Imaging..- PSGM-TR: A Transformer-Based Approach for Pulmonary Segment Segmentation Using Gaussian Mixture Models..- A Unified Pipeline for Explainable Gait Analysis..- ShapeKit..- Posterior Shape Models Revisited: Improving 3D Reconstructions from Partial Data Using Target-Specific Models..- Anatomically-Focused Patches for Lightweight and Explainable Knee OA Grading..- Benchmarking Evaluation Metrics for Tubular Structure Segmentation in Biomedical Images..- Large Intestine 3D Shape Refinement Using Conditional Latent Point Diffusion Models..- A Statistical 3D Stomach Shape Model for Anatomical Analysis..- AnatomyGen: Generating Anatomically Plausible Human Phantoms at High Resolution..- Template-Based Cortical Surface Reconstruction with Minimal Energy Deformation..- Spherical Brownian Bridge Diffusion Models for Conditional Cortical Thickness Forecasting..- OsteoDeform: Osteophyte-Aware Shape Deformations of Distal Femur Models for Surgical Planning in Total Knee Arthroplasty..- Spline-based shape compression for interventional device tracking..- End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data..- A Simple Modality-Agnostic Representation for Scoliosis Phenotyping..- US-X Complete: A Multi-Modal Approach to Anatomical 3D Shape Recovery..- A Scalable Toolkit for Modeling 3D Surface-based Brain Geometry..- Parametric shape models for vessels learned from segmentations via differentiable voxelization..- Deep Learning Enables Large-Scale Shape and Appearance Modeling in Total-Body DXA Imaging..- GReAT: leveraging geometric artery data to improve wall shear stress assessment..- Shape vs flow: a 2D statistical shape analysis of the projection of common iliac veins in patients with deep vein thrombosis..- Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation..- Point Set Registration Metrics Reloaded for Computer-Assisted Surgery..- Benchmark-Ready 3D Anatomical Shape Classification..- SimCortex: Collision-free Simultaneous Cortical Surfaces Reconstruction.
£44.99
£62.99
John Wiley & Sons Inc Pixels Paintings
Book SynopsisThis book is a collection representing some of the most powerful and useful computer techniques in the service of art.Table of ContentsList of Figures xxi List of Tables xlv List of Algorithms xlvii Preface xlix Lorenzo Lotto lviii Giovanni Morelli and the birth of "scientific" connoisseurship lix Overview lxi Intended audience lxii Prerequisites lxiii Acknowledgements lxiv 1 Digital imaging 1 1.1 Introduction 1 1.2 Electromagnetic radiation and light 4 1.3 Interaction of electromagnetic radiation with art materials 7 1.4 Cameras and scanners 9 1.4.1 Cameras 10 1.4.2 Flatbed scanners 11 1.5 Parameters for image acquisition in the visible 12 Billy Pappas 13 1.5.1 Spatial resolution 15 1.5.2 Bit depth 16 1.5.3 Dynamic range and contrast 17 1.6 Reading digital images of art on–screen 18 1.6.1 Reading a digital image of Leonardo's La Bella Principessa 22 Leonardo da Vinci 22 1.7 Infrared photography and reflectography 25 1.8 Ultraviolet imaging 26 1.9 Multispectral and hyperspectral imaging 27 1.9.1 Hyperspectral imaging of the Archimedes Palimpsest 30 1.10 X-radiographic imaging 32 1.11 Fluorescence imaging 35 1.12 Capture of three–dimensional surfaces of art 37 1.12.1 Raking illumination 38 1.12.2 Reflectance transformation imaging (RTI) 40 1.12.3 Stereographic imaging 42 1.13 Optical coherence tomography (OCT) 43 1.14 Raman spectroscopic imaging and X-ray fluorescence imaging 45 1.14.1 Raman spectroscopic imaging (RSI) 45 1.14.2 X-ray fluorescence imaging (XRF) 46 1.15 Summary 47 1.16 Bibliographical remarks 49 2 Image processing 53 2.1 Introduction 53 2.2 Pixel–based image processing 57 2.3 Region–based image processing 61 2.3.1 Linear image processing 62 2.3.2 Nonlinear region–based image processing 63 2.3.3 Color quantization 64 2.3.4 Edge and line detection 69 2.3.5 Dilation and erosion 71 2.3.6 Skeletonization 72 2.4 Inpainting 72 2.5 Feature extraction 74 2.5.1 Keypoint extraction 75 2.5.2 Craquelure and crazing analysis 78 2.5.3 Computational tests for counterproofing by Jan van der Heyden 81 Jan van der Heyden 83 2.6 Segmentation 86 2.6.1 Deep nets for image segmentation 88 2.7 Geometric transformations 95 2.8 Chamfer transform and Chamfer distance 101 2.8.1 Tests for copying of Jan van Eyck's portraits of Niccolò Albergati 103 2.9 Discrete Fourier and wavelet transforms 111 2.9.1 Discrete Fourier transform (DFT) 111 2.9.2 Canvas support weave analysis 114 2.9.3 Discrete wavelet transform (DWT) 116 2.10 Compositing and integrating art images 118 2.10.1 Image compositing 118 2.10.2 Superresolution 119 2.11 Image separation 123 2.12 Summary 123 2.13 Bibliographical remarks 125 3 Color analysis 129 3.1 Introduction 129 3.2 Visible–light spectra and color appearance 132 3.3 Overview of human color vision 133 3.3.1 Properties of color descriptions 134 3.3.2 Opponent color processing and unique hues 137 3.3.3 Humanist descriptions of color 138 3.3.4 Spatial aspects of color perception 139 Josef Albers 140 3.3.5 Color and lightness constancy and brightness perception 141 3.3.6 Quantitative descriptions and additive color mixing 141 3.3.7 Representing artists' palettes 145 3.4 Physics of color in art materials 147 3.4.1 Pigments and color appearance 147 3.5 Representing color arising from mixing paints 151 3.5.1 Identifying pigments in artworks based on spectra 152 3.6 Digital rejuvenation of pigment colors 154 3.6.1 Digital rejuvenation of faded artworks 157 Georges Seurat 158 3.7 Digital cleaning of paintings 160 3.8 Summary 164 3.9 Bibliographical remarks 165 4 Brush stroke and mark analysis 171 4.1 Introduction 171 Cy Twombly 173 4.2 Analysis of printed lines and marks 175 Katsushika Hokusai 178 4.3 Inferring tools from marks 182 Sheila Waters 184 4.3.1 Analysis of brush strokes 185 4.3.2 Segmenting and isolating brush strokes computationally 187 4.3.3 Extracting opaque marks in multiple layers 189 Vincent Willem van Gogh 193 4.3.4 Visual evidence of authorship of Pollock's drip paintings 194 Jackson Pollock 195 4.3.5 Extracting layers of translucent brush strokes 195 4.4 Characterizing the shapes of strokes and marks 203 4.5 Global methods for inferring sequences of marks in paintings 206 4.6 Summary 208 4.7 Bibliographical remarks 208 5 Perspective and geometric analysis 211 5.1 Introduction 211 5.2 Projective geometry 214 5.2.1 The mathematics of projection 216 5.2.2 One–point, two–point, and three–point perspectives 222 5.2.3 Parallel or orthographic perspective in Asian art 223 5.3 Estimating the center of projection 224 5.3.1 Foreshortening and size comparisons of depicted objects 230 Piero della Francesca 231 5.3.2 Cross–ratio analysis 232 5.3.3 Estimating the center of projection from object sizes 234 5.4 Estimating geometric accuracy in artworks 235 5.4.1 Hans Memling's Flower Still-Life 235 Hans Memling 237 5.4.2 The carpet in Lorenzo Lotto's Husband and Wife 238 5.4.3 The chandelier in the Arnolfini Portrait 238 Jan van Eyck 243 5.4.4 Warping Andrea Mantegna's Lamentation of Christ to make consistent perspective 251 5.4.5 Dewarping the murals in Sennedjem's Tomb 252 5.4.6 Warping de Chirico's Ariadne to make consistent perspective 255 Giorgio de Chirico 256 5.4.7 Robert Campin and workshop's Mérode Altarpiece 257 Robert Campin 258 5.5 Slant anamorphic art 260 Ed Ruscha (Edward Joseph Ruscha IV) 260 5.5.1 Hans Holbein's The Ambassadors 263 Hans Holbein 263 5.6 Inferring depth from projected images 264 5.6.1 Computing a three–dimensional model from one perspective image 265 Masaccio 266 5.6.2 Computing a three–dimensional model from two perspective images 267 5.7 Summary 271 5.8 Bibliographical remarks 272 6 Optical analysis 275 6.1 Introduction 275 6.2 Reflection and refraction 277 6.3 Plane mirrors 278 6.3.1 Virtual image formation by plane mirrors 279 6.3.2 Depictions of plane mirrors in art 281 6.3.3 Diego Velázquez’s Las Meninas 283 Diego Velázquez 284 6.4 Convex spherical mirrors 288 6.4.1 Virtual image formation by convex spherical mirrors 290 6.4.2 Jan van Eyck’s Portrait of Giovanni Arnolfini and his Wife 292 6.4.3 Claude glass 297 6.4.4 Parmigianino’s Self–Portrait in a Convex Mirror 298 Parmigianino (Girolamo Francesco Maria Mazzola) 298 6.4.5 Hans Memling's Virgin and Child and Maarten van Nieuwenhove 304 6.4.6 Dewarping images in generalized cylindrical mirrors 308 6.5 Conical and cylindrical mirrors and anamorphic art 312 6.5.1 Conical mirror anamorphic art 313 6.5.2 Cylindrical mirror anamorphic art 317 6.6 Concave spherical mirrors 318 6.6.1 Virtual image formation by concave mirrors 320 6.6.2 Real image formation by concave mirrors 322 6.7 Converging lenses 323 6.7.1 Virtual image formation by converging lenses 325 6.7.2 Real image formation by convex lenses 327 6.8 Camera lucida and camera obscura 328 6.8.1 Camera lucida 328 6.8.2 Camera obscura 331 6.8.3 Depth of field, depth of focus, and blur spots 333 6.9 Optical projections and the creation of art 336 6.9.1 Jan van Eyck's Portrait of Giovanni Arnolfini and his wife 337 6.9.2 Caravaggio's Supper at Emmaus 342 6.9.3 Lorenzo Lotto's Husband and Wife 345 6.9.4 Johannes Vermeer's Lady at the Virginals with a Gentleman 349 Johannes Vermeer 349 6.9.5 Canaletto's Piazza San Marco 363 Canaletto (Giovanni Antonio Canal) 364 6.9.6 Photorealists 364 Philip Barlow 366 6.10 Refraction and nonimaging optics in art 366 6.10.1 Leonardo's Salvator Mundi 366 6.11 Summary 371 6.12 Bibliographical remarks 372 7 Lighting analysis 377 7.1 Introduction 377 7.2 Basic shadows 381 7.2.1 General classes of lighting analysis methods 383 7.3 Cast–shadow analysis 383 7.3.1 Illumination from two or more point-sources 388 7.3.2 Cast–shadow analysis under geometric constraints 388 7.4 Lighting information from highlights 389 7.4.1 Illumination direction from highlights on simple estimated shapes 393 7.5 The optics of diffuse reflections 394 7.6 Inferring illumination from plane surfaces 396 Georges de la Tour 398 7.7 Interreflection 400 7.8 Occluding–contour algorithms 401 7.8.1 Single–point occluding–contour algorithm 403 7.8.2 General occluding–contour algorithm 405 Caravaggio (Michelangelo Merisi da Caravaggio) 407 7.8.3 Lightfield occluding–contour algorithm 408 Garth Herrick 409 7.8.4 Theory of the lightfield occluding–contour algorithm 410 7.8.5 Application of the lightfield occluding–contour algorithm 415 7.9 Computer graphics for the analysis of lighting 418 7.9.1 Georges de la Tour's Christ in the Carpenter's Studio (model) 419 7.9.2 Johannes Vermeer's Girl with a Pearl Earring 421 7.9.3 René Magritte's The Menaced Assassin 422 7.9.4 Bidirectional reflectance distribution functions (BRDFs) 424 7.9.5 Caravaggio's The Calling of St. Matthew 425 7.10 Shape–from–shading algorithms 426 7.10.1 Shape–from–shading by deep neural networks 429 7.10.2 Shape–from–shading for estimating both illumination and depth 430 7.11 Integrating lighting estimates 433 7.11.1 Integrating one–dimensional lighting estimates 433 7.11.2 Integrating two–dimensional lighting estimates 436 7.12 Lighting analysis for dating depicted scenes 439 7.13 Summary 442 7.14 Bibliographical remarks 444 8 Object analysis 449 8.1 Introduction 449 8.2 Image–based object classification 452 8.2.1 Feature–based object recognition 452 8.3 Feature–based analysis of faces and bodies 454 8.3.1 Feature–based analysis of body pose 464 8.3.2 Feature–based analysis of head poses 466 8.4 Deep neural network–based object recognition 468 Jacques-Louis David 472 8.4.1 Transfer training 472 8.5 Summary 474 8.6 Bibliographical remarks 475 9 Style and composition analysis 477 9.1 Introduction 477 9.2 Automatic classification of style 480 9.3 Compositional balance 482 9.3.1 Computational balance of actors 485 9.4 Geometric properties of composition 486 9.4.1 Design in Piet Mondrian's Neoplastic paintings 487 Piet Mondrian 487 9.5 Analysis of trends and similarities in artistic style 497 9.5.1 Trends in landscape compositions 498 9.5.2 Large–scale trends in the development of style 502 9.5.3 Graph representations of stylistic similarities 503 9.6 Style transfer 505 9.6.1 Style transfer by deep networks 505 9.6.2 Rejuvenating tapestries 506 9.6.3 Coloration of black–and–white photographs of artworks 507 9.6.4 Style transfer for visualizing underdrawings 509 9.7 Recovering Rembrandt's complete The Night Watch 513 Rembrandt 514 9.8 Computational generation of images for art analysis 516 9.8.1 Computational recovery of lost artworks 518 9.9 Summary 521 9.10 Bibliographical remarks 522 10 Semantic analysis 525 10.1 Introduction 525 Jacques-Louis David 528 10.2 Semantics and visual art 534 10.2.1 Natural language processing and knowledge representation 536 10.3 Meaning through associations 538 10.3.1 Signifiers and signifieds 538 10.4 Semantics of color 544 10.5 Identifying saints by their attributes 546 Andrea del Verrocchio 549 10.6 Learning associations between signifiers and signifieds 550 Harmen Steenwijck 551 10.7 Meaning through artistic style 554 10.7.1 Context in the creation of meaning 556 10.8 Automatic image captioning and question answering 557 10.8.1 Image captioning 557 10.8.2 Automatic answering of questions about artworks 559 10.9 Meaning through shape relations and associations 563 Rogier van der Weyden 563 10.9.1 Recognizing meaning–bearing stories 565 Albrecht Dürer 567 10.10 Summary 568 10.11 Bibliographical remarks 569 Appendix 573 A Symbols, acronyms, and mathematical notation 573 A.1 Mathematical notation, definitions, and operations 573 A.2 Solving simultaneous linear equations 578 A.3 Lagrange optimization 579 A.4 Basis functions 580 A.5 Discrete Fourier analysis and synthesis 580 A.6 Discrete wavelet transform 582 A.7 Spherical harmonics 582 B Probability 584 B.1 Accuracy, precision, and recall 585 B.2 Conditional probability 585 B.3 The definition of information 586 B.4 Hidden Markov models (HMMs) 586 C Bayes' theorem and reasoning about uncertainty 588 C.1 Statistical independence 588 C.2 Maximum likelihood estimation 589 C.3 Bias and variance 591 C.4 Intersection over Union metric 592 D Deep neural networks 593 E Ray tracing and image formation in mirrors and lenses 596 E.1 Converging lenses 596 E.2 Diverging lenses 599 E.3 Mirrors 600 E.4 The focal length and radius of curvature of a spherical mirror 602 E.5 Spherical versus parabolic mirrors 603 F Resources 604 Epilog 607 Glossary 609 Bibliography 615 Figure credits 673 Timeline of artists 682 Index of artists 683 Index 687 About the book 713
£119.70
De Gruyter Computational Intelligence for Managing Pandemics
Book Synopsis
£96.75
De Gruyter Augmented and Virtual Reality in Industry 5.0
Book SynopsisThis edited volume collects a series of studies concerning the most recent developments in the industrial applications of augmented and virtual reality. Each chapter outlines the most recent advancements in the theory and applications of augmented and virtual reality to different sectors of technology, industry and society. The book thus contributes to a study of the interaction between humans and machines in Industry 5.0.
£97.50
Springer Fachmedien Wiesbaden Computergrafik: Band I des Standardwerks
Book SynopsisDas Buch „Computergrafik“ vermittelt Ihnen breites Wissen über die Generierung und Verarbeitung von digitalen Bildern Sie studieren Informatik oder haben als Ingenieur beruflich mit Bildverarbeitung zu tun? Dann ist das Buch „Computergrafik“ das richtige Standardwerk für Sie. Es beschäftigt sich mit allen Bereichen dieses Themenfelds, darunter interaktiven 3D-Computergrafiken, Beleuchtungsmodellen, Szenengraphen und Cull-Algorithmen. Dabei spielt es keine Rolle, ob sie sich bereits längere Zeit mit dem Thema beschäftigen oder noch unerfahren sind. Die Autoren versorgen Fortgeschrittene und Einsteiger mit theoretischem Wissen, welches sich auch auf die Praxis übertragen lässt. Die vierte Auflage wurde erweitert und beschäftigt sich zusätzlich mit der neuen Grafik-API Vulkan. Dieses Buch ist der erste Band eines Standardwerks über Computergrafik und Bildverarbeitung. Für einen ganzheitlichen Zugang zum Thema empfehlen wir die Lektüre beider Werke.Table of ContentsZusammenhang Computergrafik – Bildverarbeitung - Interaktive 3D-Computergrafik - Anwendungen interaktiver 3D-Computergrafik - Einführung in die 3D-Computergrafik - Geometrische Grundobjekte - Koordinatensysteme und Transformationen - Verdeckung - Farbe, Transparenz und Farbmischung - Anti-Aliasing - Nebel und atmosphärische Effekte - Beleuchtung und Schattierung - Texturen - Schatten - Animationen - Beschleunigungsverfahren - GPU Programmierung mit CUDA und OpenCL
£39.99
World Scientific Publishing Co Pte Ltd Computer Vision In Medical Imaging
Book SynopsisThe major progress in computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and predication of diseases. Computer vision can exploit texture, shape, contour and prior knowledge along with contextual information from image sequence and provide 3D and 4D information that helps with better human understanding. Many powerful tools have been available through image segmentation, machine learning, pattern classification, tracking, reconstruction to bring much needed quantitative information not easily available by trained human specialists. The aim of the book is for both medical imaging professionals to acquire and interpret the data, and computer vision professionals to provide enhanced medical information by using computer vision techniques. The final objective is to benefit the patients without adding to the already high medical costs.Table of ContentsIntroduction (C H Chen); Theory and Methodologies (including Classification, Tracking, etc.): Distribution Matching Approaches to Medical Image Segmentation (Ismail Ben Ayed); Digital Pathology Imaging - A New Frontier in Medical Imaging (Bikash Sabata, Chukka Srinivas, Pascal Bamford and Jerry Fernandez); Adaptive Shape Prior Modeling via Online Dictionary Learning (Shaoting Zhang, Yiqiang Zhan, Yan Zhou and Dimitris Metaxas); Feature-Centric Lesion Detection and Retrieval for Assisting Lung Disease Diagnosis(Yang Song, etc.); A Joint Acquisition, Reconstruction and Estimation Paradigm for Quantitative Magnetic Resonance Imaging (Joseph Dagher); Fuzzy Level Set Methods for Medical Image Segmentation (Li, Bing Nan); 2D, 3D Reconstructions/Imaging Algorithms, Systems & Sensor Fusion: Model-Based Tomographic Image Reconstruction (Amir Rosenthal); The Fusion of Three-Dimensional Quantitative Coronary Angiography and Intracoronary Imaging Devices for Coronary Interventions (S T Tu); Iterative 3D Reconstruction Approach for SPECT Imaging (Baoming Hong); 3D Ultrasound Volume Reconstruction Using the Direct Frame Interpolation Method (Sergei Koptenko, etc.); Deconvolution Technique for Enhancing and Classifying the Retinal Images (Uvais Qidwai); Medical Ultrasound Digital Signal Processing in the GPU Computing Era (Marcin Lewandowski); Designing Medical Imaging Processing Algorithms for GPU Based Computing (Mathias Broxvall and Marios Daotis); Specific Image Processing and Computer Vision Methods for Different Imaging Modalities Including IVUS, PET, MRI, etc.: Computer Vision in Interventional Cardiology (Kendall Waters); On Reproducible Coronary Plaque Quantification by Multiscale Computed Tomography (Nico Bruining, et al.); Image Processing for Brain Diffusion MRI (Ali Tabesh); On Compressive Sensing in MRI (Ali Bilgin); Kinetic and Parametric Analysis of Dynamic Brain PET Studies (Ronald Boellaard); On Hierarchical Statistical Shape Models with Application to Brain MRI (Juan Cerrolaza, etc.); Advanced PDE based Methods for Automatic Quantification of Cardiac Function and Scar from MRI (Cristiana Corsi, et al.); On Automated IVUS Segmentation using a Deformable Template Model with Feature Tracking (Prakash Manandhar and C H Chen).
£123.50
John Wiley & Sons Inc Computer Vision for Structural Dynamics and
Book SynopsisProvides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring This book is the first to fill the gap between scientific research of computer vision and its practical applications for structural health monitoring (SHM). 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 tTable of ContentsList of Figures ix List of Tables xv Series Preface xvii Preface xix About the Companion Website xxi 1 Introduction 1 1.1 Structural Health Monitoring: A Quick Review 1 1.2 Computer Vision Sensors for Structural Health Monitoring 3 1.3 Organization of the Book 7 2 Development of a Computer Vision Sensor for Structural Displacement Measurement 11 2.1 Vision Sensor System Hardware 11 2.2 Vision Sensor System Software: Template-Matching Techniques 15 2.2.1 Area-Based Template Matching 16 2.2.2 Feature-Based Template Matching 20 2.3 Coordinate Conversion and Scaling Factors 22 2.3.1 Camera Calibration Method 23 2.3.2 Practical Calibration Method 25 2.4 Representative Template Matching Algorithms 28 2.4.1 Intensity-Based UCC Technique 28 2.4.2 Gradient-Based Robust OCM Technique 33 2.4.3 Vision Sensor Software Package and Operation 39 2.5 Summary 40 3 Performance Evaluation Through Laboratory and Field Tests 43 3.1 Seismic Shaking Table Test 43 3.2 Shaking Table Test of Frame Structure 1 46 3.2.1 Test Description 46 3.2.2 Subpixel Resolution 47 3.2.3 Performance When Tracking Artificial Targets 48 3.2.4 Performance When Tracking Natural Targets 49 3.2.5 Error Quantification 51 3.2.6 Evaluation of OCM and UCC Robustness 51 3.3 Seismic Shaking Table Test of Frame Structure 2 56 3.4 Free Vibration Test of a Beam Structure 59 3.4.1 Test Description 59 3.4.2 Evaluation of the Practical Calibration Method 60 3.5 Field Test of a Pedestrian Bridge 63 3.6 Field Test of a Highway Bridge 66 3.7 Field Test of Two Railway Bridges 67 3.7.1 Test Description 69 3.7.2 Daytime Measurements 72 3.7.3 Nighttime Measurements 72 3.7.4 Field Performance Evaluation 75 3.8 Remote Measurement of the Vincent Thomas Bridge 81 3.9 Remote Measurement of the Manhattan Bridge 82 3.10 Summary 87 4 Application in Modal Analysis, Model Updating, and Damage Detection 89 4.1 Experimental Modal Analysis 91 4.1.1 Modal Analysis of a Frame 91 4.1.2 Modal Analysis of a Beam 97 4.2 Model Updating as a Frequency-Domain Optimization Problem 101 4.3 Damage Detection 108 4.3.1 Mode Shape Curvature-Based Damage Index 108 4.3.2 Test Description 109 4.3.3 Damage Detection Results 110 4.4 Summary 112 5 Application in Model Updating of Railway Bridges under Trainloads 115 5.1 Field Measurement of Bridge Displacement under Trainloads 116 5.2 Formulation of the Finite Element Model 118 5.2.1 Modeling the Train-Track-Bridge Interaction 118 5.2.2 Finite Element Model of the Railway Bridge 120 5.3 Sensitivity Analysis and Finite Element Model Updating 121 5.3.1 Model Updating as a Time-Domain Optimization Problem 122 5.3.2 Sensitivity Analysis of Displacement and Acceleration Responses 123 5.3.3 Finite Element Model Updating 127 5.4 Dynamic Characteristics of Short-Span Bridges under Trainloads 130 5.5 Summary 136 6 Application in Simultaneously Identifying Structural Parameters and Excitation Forces 139 6.1 Simultaneous Identification Using Vision-Based Displacement Measurements 140 6.1.1 Structural Parameter Identification as a Time-Domain Optimization Problem 141 6.1.2 Force Identification Based on Structural Displacement Measurements 142 6.1.3 Simultaneous Identification Procedure 144 6.2 Numerical Example 146 6.2.1 Robustness to Noise and Number of Sensors 147 6.2.2 Robustness to Initial Stiffness Values 150 6.2.3 Robustness to Damping Ratio Values 150 6.3 Experimental Validation 154 6.3.1 Test Description 154 6.3.2 Identification Results 155 6.4 Summary 157 7 Application in Estimating Cable Force 171 7.1 Vision Sensor for Estimating Cable Force 172 7.1.1 Vibration Method 172 7.1.2 Procedure for Vision-Based Cable Tension Estimation 173 7.2 Implementation in the Hard Rock Stadium Renovation Project 174 7.2.1 Hard Rock Stadium 175 7.2.2 Test Description 176 7.2.3 Estimating and Validating Cable Force 178 7.3 Implementation in the Bronx-Whitestone Bridge Suspender Replacement Project 184 7.3.1 Bronx-Whitestone Bridge 184 7.3.2 Estimating Suspender Tension 185 7.4 Summary 187 8 Achievements, Challenges, and Opportunities 191 8.1 Capabilities of Vision-Based Displacement Sensors: A Summary 191 8.1.1 Artificial vs. Natural Targets 192 8.1.2 Single-Point vs. Multipoint Measurements 192 8.1.3 Pixel vs. Subpixel Resolution 193 8.1.4 2D vs. 3D Measurements 194 8.1.5 Real Time vs. Post Processing 194 8.2 Sources of Error in Vision-Based Displacement Sensors 195 8.2.1 Camera Motion 196 8.2.2 Coordinate Conversion 197 8.2.3 Hardware Limitations 198 8.2.4 Environmental Sources 198 8.3 Vision-Based Displacement Sensors for Structural Health Monitoring 199 8.3.1 Dynamic Displacement Measurement 199 8.3.2 Modal Property Identification 201 8.3.3 Model Updating and Damage Detection 202 8.3.4 Cable Force Estimation 203 8.4 Other Civil and Structural Engineering Applications 204 8.4.1 Automated Machine Visual Inspection 204 8.4.2 Onsite Construction Tracking and Safety Monitoring 206 8.4.3 Vehicle Load Estimation 206 8.4.4 Other Applications 207 8.5 Future Research Directions 208 Appendix: Fundamentals of Digital Image Processing Using MATLAB 211 A.1 Digital Image Representation 211 A.2 Noise Removal 214 A.3 Edge Detection 216 A.4 Discrete Fourier Transform 217 References 221 Index 229
£100.76
Apress Computer Vision Metrics
Book SynopsisComputer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features.Table of ContentsChapter 1. Image Capture and RepresentationChapter 2. Image Pre-ProcessingChapter 3. Global and Regional FeaturesChapter 4. Local Feature Design Concepts, Classification, and LearningChapter 5. Taxonomy Of Feature Description AttributesChapter 6. Interest Point Detector and Feature Descriptor SurveyChapter 7. Ground Truth Data, Data, Metrics, and AnalysisChapter 8. Vision Pipelines and OptimizationsAppendix A. Synthetic Feature AnalysisAppendix B. Survey of Ground Truth DatasetsAppendix C. Imaging and Computer Vision ResourcesAppendix D. Extended SDM Metrics
£22.32
Nova Science Publishers Inc Face Recognition: Methods, Applications &
Book Synopsis
£159.99
Springer London Ltd Advanced Algorithmic Approaches to Medical Image
Book SynopsisMedical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound. This book focuses primarily on model-based segmentation techniques, which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors working in industry and academia, and presents new material.Table of Contents1. Principles of Image Generation.- 1.1 Introduction.- 1.2 Ultrasound Image Generation.- 1.2.1 The Principle of Pulse-Echo Ultrasound Imaging.- 1.2.2 B-Scan Quality and the Ultimate Limits.- 1.2.3 Propagation-Related Artifacts and Resolution Limits.- 1.2.3 Attenuation-Related Artifacts.- 1.3 X-Ray Cardiac Image Generation.- 1.3.1 LV Data Acquisition System Using X-Rays.- 1.3.2 Drawbacks of Cardiac Catheterization.- 1.4 Magnetic Resonance Image Generation.- 1.4.1 Physical Principles of Nuclear Magnetic Resonance.- 1.4.2 Basics of Magnetic Resonance Imaging.- 1.4.3 Gradient-Echo (GRE).- 1.4.4 The Latest Techniques for MR Image Generation.- 1.4.5 3-D Turbo FLASH (MP-RAGE) Technique.- 1.4.6 Non-Rectilinear k-Space Trajectory: Spiral.- 1.4.7 Fat Suppression.- 1.4.8 High Speed MRI: Perfusion-Weighted.- 1.4.9 Time of Flight (TOF) MR Angiography.- 1.4.10 Fast Spectroscopic Imaging.- 1.4.11 Recent MR Imaging Techniques.- 1.5 Computer Tomography Image Generation.- 1.5.1 Fourier Reconstruction Method.- 1.6 Positron-Emission Tomography Image Generation.- 1.6.1 Underlying Principles of.- 1.6.2 Usage of PET in Diagnosis.- 1.6.3 Fourier Slice Theorem.- 1.6.4 The Reconstruction Algorithm in PET.- 1.6.5 Image Reconstruction Using Filtered Back-Projection.- 1.7 Comparison of Imaging Modalities: A Summary.- 1.7.1 Acknowledgements.- 2. Segmentation in Echocardiographic Images.- 2.1 Introduction.- 2.2 Heart Physiology and Anatomy.- 2.2.1 Cardiac Function.- 2.2.2 Standard LV Views in 2-DEs.- 2.2.3 LV Function Assessment Using 2-DEs.- 2.3 Review of LV Boundary Extraction Techniques Applied to Echocardiographic Data.- 2.3.1 Acoustic Quantification Techniques.- 2.3.2 Image-Based Techniques.- 2.3.3 2-DE Image Processing Techniques.- 2.4Automatic Fuzzy Reasoning-Based Left Ventricular Center Point Extraction.- 2.4.1 LVCP Extraction System Overview.- 2.4.2 Stage 1: Pre-Processing.- 2.4.3 Stage 2: LVCP Features Fuzzification.- 2.4.4 Template Matching.- 2.4.5 Experimental Results.- 2.4.6 Conclusion.- 2.5 A New Edge Detection in the Wavelet Transform Domain.- 2.5.1 Multiscale Edge Detection and the Wavelet Transform.- 2.5.2 Edge Detection Based on the Global Maximum of Wavelet Transform (GMWT).- 2.5.3 GMWT Performance Analysis and Comparison.- 2.6 LV Segmentation System.- 2.6.1 Overall Reference.- 2.6.2 3D Non-Uniform Radial Intensity Sampling.- 2.6.3 LV Boundary Edge Detection on 3D Radial Intensity Matrix.- 2.6.4 Post-Processing of the Edges and Closed LVE Approximation.- 2.6.5 Automatic LV Volume Assessment.- 2.7 Conclusions.- 2.8 Acknowledgments.- 3. Cardiac Boundary Segmentation.- 3.1 Introduction.- 3.2 Cardiac Anatomy and Data Acquisitions for MR, CT, Ul-trasound and X-Rays.- 3.2.1 Cardiac Anatomy.- 3.2.2 Cardiac MR, CT, Ultrasound and X-Ray Acquisitions.- 3.3 Low- and Medium-Level LV Segmentation Techniques.- 3.3.1 Smoothing Image Data.- 3.3.2 Manual and Semi-Automatic LV Thresholding.- 3.3.3 LV Dynamic Thresholding.- 3.3.4 Edge-Based Techniques.- 3.3.5 Mathematical Morphology-Based Techniques.- 3.3.6 Drawbacks of Low-Level LV Segmentation Techniques.- 3.4 Model-Based Pattern Recognition Methods for LV Modeling.- 3.4.1 LV Active Contour Models in the Spatial and Temporal Domains.- 3.4.2 Model-Based Pattern Recognition Learning Methods.- 3.4.3 Polyline Distance Measure and Performance Terms.- 3.4.4 Data Analysis Using IdCM, InCM and the Greedy Method.- 3.5 Left Ventricle Apex Modeling: A Model-Based Approach.- 3.5.1 Longitudinal Axis and Apex Modeling.- 3.5.2 Ruled Surface Model.- 3.5.3 Ruled Surface sr and its Coefficients.- 3.5.4 Estimation of Robust Coefficients and Coordinates of the Ruled Surface.- 3.5.5 Experiment Design.- 3.5.6 Analytical Error Measure, AQin for Inlier Data.- 3.5.7 Experiments, Results and Discussions.- 3.5.8 Conclusions on LV Apex Modeling.- 3.6 Integration of Low-Level Features in LV Model-Based Cardiac Imaging: Fusion of Two Computer Vision Systems.- 3.7 General Purpose LV Validation Technique.- 3.8 LV Convex Hulling: Quadratic Training-Based Point Modeling.- 3.8.1 Quadratic Vs. Linear Optimization for Convex Hulling.- 3.9 LV Eigen Shape Modeling.- 3.9.1 Procrustes Superposition.- 3.9.2 Dimensionality Reduction Using Constraints for Joint.- 3.10 LV Neural Network Models.- 3.11 Comparative Study and Summary of the Characteristics of Model-Based Techniques.- 3.11.1 Characteristics of Model-Based LV Imaging.- 3.12 LV Quantification: Wall Motion and Tracking.- 3.12.1 LV Wall Motion Measurements.- 3.12.2 LV Volume Measurements.- 3.12.3 LV Wall Motion Tracking.- 3.13 Conclusions.- 3.13.1 Cardiac Hardware.- 3.13.2 Cardiac Software.- 3.13.3 Summary.- 3.13.4 Acknowledgments.- 4. Brain Segmentation Techniques.- 4.1 Introduction.- 4.1.1 Human Brain Anatomy and the MRI System.- 4.1.2 Applications of Brain Segmentation.- 4.2 Brain Scanning and its Clinical Significance.- 4.3 Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.3.1 Atlas-Based and Threshold-Based Techniques.- 4.3.2 Cortical Segmentation Using Probability-Based Techniques.- 4.3.3 Clustering-Based Cortical Segmentation Techniques.- 4.3.4 Mathematical Morphology-Based Cortical Segmentation Techniques.- 4.3.5 Prior Knowledge-Based Techniques.- 4.3.6 Texture-Based Techniques.- 4.3.7 Neural Network-Based Techniques.- 4.3.8 Regional Hyperstack: Fusion of Edge-Diffusion with Region-Linking.- 4.3.9 Fusion of Probability-Based with Edge Detectors, Connectivity and Region-Growing.- 4.3.10 Summary of Region-Based Techniques: Pros and Cons.- 4.4 Boundary/Surface-Based 2-D and 3-D Cortical Segmentation Techniques: Edge, Reconstruction, Parametric and Geometric Snakes/Surfaces.- 4.4.1 Edge-Based Cortical-Boundary Estimation Techniques.- 4.4.2 3-D Cortical Reconstruction From 2-D Serial Cross-Sections (Bourke/Victoria).- 4.4.3 2-D and 3-D Parametric Deformable Models for Cortical Boundary Estimation: Snakes, Fitting, Constrained, Ribbon, T-Surface, Connectedness.- 4.4.4 2-D and 3-D Geometric Deformable Models.- 4.4.5 A Note on Isosurface Extraction (Lorensen/GE).- 4.4.6 Summary of Boundary/Surface-Based Techniques: Pros and Cons.- 4.5 Fusion of Boundary/Surface with Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.5.1 2-D/3-D Regional Parametric Boundary: Fusion of Boundary with Classification (Kapur/MIT).- 4.5.2 Regional Parametric Surfaces: Fusion of Surface with Clustering (Xu/JHU).- 4.5.3 2-D Regional Geometric Boundary: Fusion of Boundary with Clustering for Cortical Boundary Estimation (Suri/Marconi).- 4.5.34 3-D Regional Geometric Surfaces: Fusion of Geometric Surface with Probability-Based Voxel Classification (Zeng/Yale).- 4.5.5 2-D/3-D Regional Geometric Surface: Fusion of Geometric Boundary/Surface with Global Shape Information (Leventon/MIT).- 4.5.6 2-D/3-D Regional Geometric Surface: Fusion of Boundary/Surface with Bayesian-Based Pixel Classification (Barillot/IRISA).- 4.5.7 Similarities/Differences Between Different Cortical Segmentation Techniques.- 4.6 3-D Visualization Using Volume Rendering and Texture Mapping.- 4.6.1 Volume Rendering Algorithm for Brain Segmentation.- 4.6.2 Texture Mapping Algorithm for Segmented Brain Visualization.- 4.7 A Note on fMRI: Algorithmic Approach for Establishing the Relationship Between Cognitive Functions and Brain Cortical Anatomy.- 4.7.1 Superiority of fMRI over PET/SPECT Imaging.- 4.7.2 Applications of fMRI.- 4.7.3 Algorithm for Superimposition of Functional and Anatomical Cortex.- 4.7.4 A Short Note on fMRI Time Course Data Analysis.- 4.7.5 Measure of Cortex Geometry.- 4.8 Discussions: Advantages, Validation and New Challenges i 2-D.- 4.8.1 Advantages of Regional Geometric Boundary/Surfaces.- 4.8.2 Validation of 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.3 Challenges in 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.4 Challenges in fMRI.- 4.9 Conclusions and the Future.- 4.9.1 Acknowledgements.- 5. Segmentation for Multiple Sclerosis Lesion.- 5.1 Introduction.- 5.2 Segmentation Techniques.- 5.2.1 Multi-Spectral Techniques.- 5.2.2 Feature Space Classification.- 5.2.3 Supervised Segmentation.- 5.2.4 Unsupervised Segmentation.- 5.2.5 Automatic Segmentation.- 5.3 AFFIRMATIVE Images.- 5.4 Image Pre-Processing.- 5.4.1 RF Inhomogeneity Correction.- 5.4.2 Image Stripping.- 5.4.3 Three Dimensional MR Image Registration.- 5.4.4 Segmentation.- 5.4.5 Flow Correction.- 5.4.6 Evaluation and Validation.- 5.5 Quantification of Enhancing Multiple Sclerosis Lesions.- 5.6 Quadruple Contrast Imaging.- 5.7 Discussion.- 5.7.1 Acknowledgements.- 6. Finite Mixture Models.- 6.1 Introduction.- 6.2 Pixel Labeling Using the Classical Mixture Model.- 6.3 Pixel Labeling Using the Spatially Variant Mixture Model.- 6.4 Comparison of CMM and SVMM for Pixel Labeling.- 6.5 Bayesian Pixel Labeling Using the SVMM.- 6.6 Segmentation Results.- 6.6.1 Computer Simulations.- 6.6.2 Application to Magnetic Resonance Images.- 6.7 Practical Aspects.- 6.8 Summary.- 6.9 Acknowledgements.- 7. MR Spectroscopy.- 7.1 Introduction.- 7.2 A Short History of Neurospectroscopic Imaging and Segmentation in Alzheimer’s Disease and Multiple Sclerosis.- 7.2.1 Alzheimer’s Disease.- 7.2.2 Multiple Sclerosis.- 7.3 Data Acquisition and Image Segmentation.- 7.3.1 Image Pre-Processing for Segmentation.- 7.3.2 Image Post-Processing for Segmentation.- 7.4 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation in Multiple Sclerosis.- 7.4.1 Automatic MRSI Segmentation and Image Processing Algorithm.- 7.4.2 Relative Metabolite Concentrations and Contribution of Gray Matter and White Matter in the Normal Human Brain.- 7.4.3 MRSI and Gadolinium-Enhanced (Gd).- 7.4.4 Lesion Load and Metabolite Concentrations by Segmentation and MRSI.- 7.4.5 MR Spectroscopic Imaging and Localization for Segmentation.- 7.4.6 Lesion Segmentation and Quantification.- 7.4.7 Magnetic Resonance Spectroscopic Imaging and Segmentation Data Processing.- 7.4.8 Statistical Analysis.- 7.5 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation of Alzheimer’s Disease.- 7.5.1 MRSI Data Acquisition Methods.- 7.5.2 H-1 MR Spectra Analysis.- 7.6 Applications of Magnetic Resonance Spectroscopic Imaging and Segmentation.- 7.6.1 Multiple Sclerosis Lesion Metabolite Characteristics and Serial Changes.- 7.6.2 zheimer’s Disease Plaque Metabolite Characteristics.- 7.7 Discussion.- 7.8 Conclusion.- 7.8.1 Acknowledgements.- 8. Fast WM/GM Boundary Estimation.- 8.1 Introduction.- 8.2 Derivation of the Regional Geometric Active Contour Model from the Classical Parametric Deformable Model.- 8.3 Numerical Implementation of the Three Speed Functions in the Level Set Framework for Geometric Snake Propagation.- 8.3.1 Regional Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.2 Gradient Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.3 Curvature Speed Term Expressed in Terms of the Level Set Function (ø).- 8.4 Fast Brain Segmentation System Based on Regional Level Sets.- 8.4.1 Overall System and Its Components.- 8.4.2 Fuzzy Membership Computation/Pixel Classification.- 8.4.3 Eikonal Equation and its Mathematical Solution.- 8.4.4 Fast Marching Method for Solving the Eikonal Equation.- 8.4.5 A Note on the Heap Sorting Algorithm.- 8.4.6 Segmentation Engine: Running the Level Set Method in the Narrow Band.- 8.5 MR Segmentation Results on Synthetic and Real Data.- 8.5.1 Input Data Set and Input Level Set Parameters.- 8.5.2 Results: Synthetic and Real.- 8.5.3 Numerical Stability, Signed Distance Transformation Computation, Sensitivity of Parameters and Speed Issues.- 8.6 Advantages of the Regional Level Set Technique.- 8.7 Discussions: Comparison with Previous Techniques.- 8.8 Conclusions and Further Directions.- 8.8.1 Acknowledgements.- 9. Digital Mammography Segmentation.- 9.1 Introduction.- 9.2 Image Segmentation in Mammography.- 9.3 Anatomy of the Breast.- 9.4 Image Acquisition and Formats.- 9.4.1 Digitization of X-Ray Mammograms.- 9.4.2 Image Formats.- 9.4.3 Image Quantization and Tree-Pyramids.- 9.5 Mammogram Enhancement Methods.- 9.6 Quantifying Mammogram Enhancement.- 9.7 Segmentation of Breast Profile.- 9.8 Segmentation of Microcalcifications.- 9.9 Segmentation of Masses.- 9.9.1 Global Methods.- 9.9.2 Edge-Based Methods.- 9.9.3 Region-Based Segmentation.- 9.9.4 ROI Detection Techniques Using a Single Breast.- 9.9.5 ROI Detection Techniques Using Breast Symmetry.- 9.9.6 Detection of Spicules.- 9.9.7 Breast Alignment for Segmentation.- 9.10 Measures of Segmentation and Abnormality Detection.- 9.11 Feature Extraction From Segmented Regions.- 9.11.1 Morphological Features.- 9.11.2 Texture Features.- 9.11.3 Other Features.- 9.12 Public Domain Databases in Mammography.- 9.12.1 The Digital Database for Screening Mammography (DDSM).- 9.12.2 LLNL/UCSF Database.- 9.12.3 Washington University Digital Mammography Database.- 9.12.4 The Mammographic Image Analysis Society (MIAS) Database.- 9.13 Classification and Measures of Performance.- 9.13.1 Classification Techniques.- 9.13.2 The Receiver Operating Characteristic Curve.- 9.14 Conclusions.- 9.15 Acknowledgements.- 10. Cell Image Segmentation for Diagnostic Pathology.- 10.1 Introduction.- 10.2 Segmentation.- 10.2.1 Feature Space Analysis.- 10.2.2 Mean Shift Procedure.- 10.2.3 Cell Segmentation.- 10.2.4 Segmentation Examples.- 10.3 Decision Support System for Pathology.- 10.3.1 Problem Domain.- 10.3.2 System Overview.- 10.3.3 Current Database.- 10.3.4 Analysis of Visual Attributes.- 10.3.5 Overall Dissimilarity Metric.- 10.3.6 Performance Evaluation and Comparisons.- 10.4 Conclusion.- 11. The Future in Segmentation.- 11.1 Future Research in Medical Image Segmentation.- 11.1.1 The Future of MR Image Generation and Physical Principles.- 11.1.2 The Future of Cardiac Imaging.- 11.2.3 The Future of Neurological Segmentation.- 11.2.4 The Future in Digital Mammography.- 11.2.5 The Future of Pathology Image Segmentation.
£169.99
Imperial College Press Dynamic Vision: From Images To Face Recognition
Book SynopsisFace recognition is a task that the human vision system seems to perform almost effortlessly, yet the goal of building computer-based systems with comparable capabilities has proven to be difficult. The task implicitly requires the ability to locate and track faces through often complex and dynamic scenes. Recognition is difficult because of variations in factors such as lighting conditions, viewpoint, body movement and facial expression. Although evidence from psychophysical and neurobiological experiments provides intriguing insights into how we might code and recognise faces, its bearings on computational and engineering solutions are far from clear. The study of face recognition has had an almost unique impact on computer vision and machine learning research at large. It raises many challenging issues and provides a good vehicle for examining some difficult problems in vision and learning. Many of the issues raised are relevant to object recognition in general.This book describes the latest models and algorithms that are capable of performing face recognition in a dynamic setting. The key question is how to design computer vision and machine learning algorithms that can operate robustly and quickly under poorly controlled and changing conditions. Consideration of face recognition as a problem in dynamic vision is perhaps both novel and important. The algorithms described have numerous potential applications in areas such as visual surveillance, verification, access control, video-conferencing, multimedia and visually mediated interaction.The book will be of special interest to researchers and academics involved in machine vision, visual recognition and machine learning. It should also be of interest to industrial research scientists and managers keen to exploit this emerging technology and develop automated face and human recognition systems. It is also useful to postgraduate students studying computer science, electronic engineering, information or systems engineering, and cognitive psychology.Trade Review"Dynamic Vision is a unique book. To my knowledge, there is no comparable book that covers the broad and complex domain of adaptive visual recognition in such a readable way. The clear presentation style helps the reader to appreciate the painstaking work involved in making the automatic recognition of faces possible - the authors were successful in providing 'a coherent and unified treatment of the issue from a computational and systems perspective' and highly recommend the book to any researcher interested in face recognition or visual recognition in general." Cognitive Systems ResearchTable of ContentsBackground: about face; perception and representation; learning under uncertainty. From sensory to meaningful perception: selective attention - where to look; a face model - what to look for; understanding pose; prediction and adaptation. Models of identity: single-view identification; multi-view identification; identifying moving faces. Perception in context: perceptual integration; beyond faces. Appendices: databases; commercial systems; mathematical details.
£67.45
Springer Nature Switzerland AG Computational Intelligence Methods for Super-Resolution in Image Processing Applications
Book SynopsisThis book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV) problem ─ super-resolution (SR). The authors present and discuss ways to apply computational intelligence (CI) methods to SR. The volume also explores the possibility of using different kinds of CV techniques to develop and enhance the tools/processes related to SR. The application areas covered include biomedical engineering, healthcare applications, medicine, histology, and material science. The book will be a valuable reference for anyone concerned with multiple multimodal images, especially professionals working in remote sensing, nanotechnology and immunology at research institutes, healthcare facilities, biotechnology institutions, agribusiness services, veterinary facilities, and universities.Table of ContentsPart I. A Panorama of Computational Intelligence in Super-Resolution Imaging.- Chapter 1. Introduction to Computational Intelligence and Super-Resolution.- Chapter 2. Review on Fuzzy Logic Systems with Super-Resolved Imaging and Metaheuristics for Medical Applications.- Chapter 3. Super-Resolution with Deep Learning Techniques-A Review.- Chapter 4. A Comprehensive Review of CAD Systems in Ultrasound and Elastography for Breast Cancer Diagnosis.- Part II. State-of-the-Art Computational Intelligence in Super-Resolution Imaging.- Chapter 5. Pictorial Image Synthesis from Text and Its Super-Resolution using Generative Adversarial Networks.- Chapter 6. Analysis of Lossy and Lossless Compression Algorithms for Computed Tomography Medical Images Based on Bat and Simulated Annealing Optimization Techniques.- Chapter 7. Super resolution-based Human-Computer Interaction System for Speech and Hearing Impaired using Real-Time Hand Gesture Recognition System.- Chapter 8. Lossy Compression of Noisy Images Using Autoencoders for Computer Vision Applications.- Chapter 9. Recognition of Handwritten Nandinagari Palm Leaf Manuscript Tex.- Chapter 10. Deep Image Prior and Structural Variation Based Super-Resolution Network for Fluorescein Fundus Angiography Images.- Chapter 11. Lightweight Spatial Geometric Models Assisting Shape Description and Retrieval and Relative Global Optimum Based Measure for Fusion.- Chapter 12. Dual-Tree Complex Wavelet Transform and Deep CNN-based Super-Resolution for Video Inpainting with Application to Object Removal and Error Concealment.- Chapter 13. Super-Resolution Imaging and Intelligent solution for Classification, Monitoring and Diagnosis of Alzheimer's Disease.- Chapter 14. Image Enhancement using Non-Local Prior and Gradient Residual Minimization for Improved Visualization of Deep Underwater Image.- Chapter 15. Relative Global Optimum Based Measure for Fusion Technique in Shearlet Transform Domain for Prognosis of Alzheimer Disease.
£142.49
Springer Nature Switzerland AG 3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
Book SynopsisThis book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.Table of ContentsI. Introduction.- II. Traditional point cloud analysis.- III. Deep-learning-based point cloud analysis.- IV. Explainable machine learning methods for point cloud analysis.- V. Conclusion and future work.
£94.99
Springer Nature Switzerland AG Moving Objects Detection Using Machine Learning
Book SynopsisThis book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.Table of ContentsChapter1. Introduction.- Chapter2. Existing Research in Video Surveillance System .- Chapter3. Background Modeling.- Chapter4. Object Tracking.- Chapter5. Summary of Book.
£49.49
Springer International Publishing AG Biomedical Signal and Image Processing with Artificial Intelligence
Book SynopsisThis book focuses on advanced techniques used for feature extraction, analysis, recognition, and classification in the area of biomedical signal and image processing. Contributions cover all aspects of artificial intelligence, machine learning, and deep learning in the field of biomedical signal and image processing using novel and unexplored techniques and methodologies. The book covers recent developments in both medical images and signals analyzed by artificial intelligence techniques. The authors also cover topics related to development based artificial intelligence, which includes machine learning, neural networks, and deep learning. This book will provide a platform for researchers who are working in the area of artificial intelligence for biomedical applications. Provides insights into medical signal and image analysis using artificial intelligence; Includes novel and recent trends of decision support system for medical research; Outlines employment of evolutionary algorithms for biomedical data, big data analysis for medical databases, and reliability, opportunities, and challenges in clinical data. Table of ContentsIntroduction.- Lung Classification for Covid-19.- Inspection and Test in Hemodialysis arena.- Applications of Artificial Intelligence in Medical Images Analysis.- Parameter efficient Epileptic seizure detection.- Early lung cancer detection by using an artificial intelligence system.- Decision Making Biomedical Support Systems.- Intelligent Image Segmentation Methods using Deep Convolutional Neural Networks.- Early detection of Dyslexia.- Artificial Intelligence Assisted Cardiac Signal Analysis for Heart Disease Prediction.- An optimal model selection for COVID 19 disease classification.- Histopathology Whole-Slide Image Analysis for Breast Cancer Detection.- Analysis and Classification of Impaired Speech.- Imaging technologies for early detection of breast cancer.- VLSI implementation of sEMG based classification for muscle activity control.- An Object Aware Hybrid U-Net for Breast Tumour Annotation.- Guide to scrutinize the analysis tool for biomedical signals.- Real-time COVID-19 Prediction using Chest X-ray and AI.- Classification of Primary Benign and Primary Malignant Focal Liver Lesions in Spatial and NSCT domain.- Ultrasound Image Analysis using AI.- Conclusion.
£94.99
