Computer vision Books

Book SynopsisThis book publishes a collection of original scientific research articles that address the state-of-art in using partial differential equations for image and signal processing. Coverage includes: level set methods for image segmentation and construction, denoising techniques, digital image inpainting, image dejittering, image registration, and fast numerical algorithms for solving these problems. Table of ContentsDigital Image Inpainting, Image Dejittering, and Optical Flow Estimation.- Image Inpainting Using a TV-Stokes Equation.- Error Analysis for H1 Based Wavelet Interpolations.- Image Dejittering Based on Slicing Moments.- CLG Method for Optical Flow Estimation Based on Gradient Constancy Assumption.- Denoising and Total Variation Methods.- On Multigrids for Solving a Class of Improved Total Variation Based Staircasing Reduction Models.- A Method for Total Variation-based Reconstruction of Noisy and Blurred Images.- Minimization of an Edge-Preserving Regularization Functional by Conjugate Gradient Type Methods.- A Newton-type Total Variation Diminishing Flow.- Chromaticity Denoising using Solution to the Skorokhod Problem.- Improved 3D Reconstruction of Interphase Chromosomes Based on Nonlinear Diffusion Filtering.- Image Segmentation.- Some Recent Developments in Variational Image Segmentation.- Application of Non-Convex BV Regularization for Image Segmentation.- Region-Based Variational Problems and Normal Alignment – Geometric Interpretation of Descent PDEs.- Fast PCLSM with Newton Updating Algorithm.- Fast Numerical Methods.- Nonlinear Multilevel Schemes for Solving the Total Variation Image Minimization Problem.- Fast Implementation of Piecewise Constant Level Set Methods.- The Multigrid Image Transform.- Minimally Stochastic Schemes for Singular Diffusion Equations.- Image Registration.- Total Variation Based Image Registration.- Variational Image Registration Allowing for Discontinuities in the Displacement Field.- Inverse Problems.- Shape Reconstruction from Two-Phase Incompressible Flow Data using Level Sets.- Reservoir Description Using a Binary Level Set Approach with Additional Prior Information About the Reservoir Model.

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Book SynopsisComputer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and tutorials held during the first two editions of the school on topics such as Recognition, Registration and Reconstruction. The chapters provide an in-depth overview of these challenging areas with key references to the existing literature.Table of ContentsIs Human Vision Any Good?.- Knowing a Good Feature When You See It: Ground Truth and Methodology to Evaluate Local Features for Recognition.- Dynamic Graph Cuts and Their Applications in Computer Vision.- Discriminative Graphical Models for Context-Based Classification.- From the Subspace Methods to the Mutual Subspace Method.- What, Where and Who? Telling the Story of an Image by Activity Classification, Scene Recognition and Object Categorization.- Semantic Texton Forests.- Multi-view Object Categorization and Pose Estimation.- A Vision-Based Remote Control.- Multi-view Multi-object Detection and Tracking.- Shape from Photographs: A Multi-view Stereo Pipeline.- Practical 3D Reconstruction Based on Photometric Stereo.

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Book SynopsisThis book offers a thorough overview of the most popular and researched meta-heuristic optimization techniques and nature-inspired algorithms. Their wide applicability makes them a hot research topic and an effi cient tool for the solution of complex optimization problems in various fi elds of sciences, engineering, and in numerous industries.

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Table of Contents List of contributors xi About the editors xiii Preface xv 1. The dramatically changing face of computer vision E.R. DAVIES 1.1 Introduction – computer vision and its origins 1 1.2 Part A – Understanding low-level image processing operators 4 1.3 Part B – 2-D object location and recognition 15 1.4 Part C – 3-D object location and the importance of invariance 29 1.5 Part D – Tracking moving objects 55 1.6 Part E – Texture analysis 61 1.7 Part F – From artificial neural networks to deep learning methods 68 1.8 Part G – Summary 86 References 87 2. Advanced methods for robust object detection ZHAOWEI CAI AND NUNO VASCONCELOS 2.1 Introduction 93 2.2 Preliminaries 95 2.3 R-CNN 96 2.4 SPP-Net 97 2.5 Fast R-CNN 98 2.6 Faster R-CNN 101 2.7 Cascade R-CNN 103 2.8 Multiscale feature representation 106 2.9 YOLO 110 2.10 SSD 112 2.11 RetinaNet 113 2.12 Detection performances 115 2.13 Conclusion 115 References 116 3. Learning with limited supervision SUJOY PAUL AND AMIT K. ROY-CHOWDHURY 3.1 Introduction 119 3.2 Context-aware active learning 120 3.3 Weakly supervised event localization 129 3.4 Domain adaptation of semantic segmentation using weak labels 137 3.5 Weakly-supervised reinforcement learning for dynamical tasks 144 3.6 Conclusions 151 References 153 4. Efficient methods for deep learning HAN CAI, JI LIN, AND SONG HAN 4.1 Model compression 159 4.2 Efficient neural network architectures 170 4.3 Conclusion 185 References 185 5. Deep conditional image generation GANG HUA AND DONGDONG CHEN 5.1 Introduction 191 5.2 Visual pattern learning: a brief review 194 5.3 Classical generative models 195 5.4 Deep generative models 197 5.5 Deep conditional image generation 200 5.6 Disentanglement for controllable synthesis 201 5.7 Conclusion and discussions 216 References 216 6. Deep face recognition using full and partial face images HASSAN UGAIL 6.1 Introduction 221 6.2 Components of deep face recognition 227 6.3 Face recognition using full face images 231 6.4 Deep face recognition using partial face data 233 6.5 Specific model training for full and partial faces 237 6.6 Discussion and conclusions 239 References 240 7. Unsupervised domain adaptation using shallow and deep representations YOGESH BALAJI, HIEN NGUYEN, AND RAMA CHELLAPPA 7.1 Introduction 243 7.2 Unsupervised domain adaptation using manifolds 244 7.3 Unsupervised domain adaptation using dictionaries 247 7.4 Unsupervised domain adaptation using deep networks 258 7.5 Summary 270 References 270 8. Domain adaptation and continual learning in semantic segmentation UMBERTO MICHIELI, MARCO TOLDO, AND PIETRO ZANUTTIGH 8.1 Introduction 275 8.2 Unsupervised domain adaptation 277 8.3 Continual learning 291 8.4 Conclusion 298 References 299 9. Visual tracking MICHAEL FELSBERG 9.1 Introduction 305 9.2 Template-based methods 308 9.3 Online-learning-based methods 314 9.4 Deep learning-based methods 323 9.5 The transition from tracking to segmentation 327 9.6 Conclusions 331 References 332 10. Long-term deep object tracking EFSTRATIOS GAVVES AND DEEPAK GUPTA 10.1 Introduction 337 10.2 Short-term visual object tracking 341 10.3 Long-term visual object tracking 345 10.4 Discussion 367 References 368 11. Learning for action-based scene understanding CORNELIA FERMÜLLER AND MICHAEL MAYNORD 11.1 Introduction 373 11.2 Affordances of objects 375 11.3 Functional parsing of manipulation actions 383 11.4 Functional scene understanding through deep learning with language and vision 390 11.5 Future directions 397 11.6 Conclusions 399 References 399 12. Self-supervised temporal event segmentation inspired by cognitive theories RAMY MOUNIR, SATHYANARAYANAN AAKUR, AND SUDEEP SARKAR 12.1 Introduction 406 12.2 The event segmentation theory from cognitive science 408 12.3 Version 1: single-pass temporal segmentation using prediction 410 12.4 Version 2: segmentation using attention-based event models 421 12.5 Version 3: spatio-temporal localization using prediction loss map 428 12.6 Other event segmentation approaches in computer vision 440 12.7 Conclusions 443 References 444 13. Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems CARLO REGAZZONI, ALI KRAYANI, GIULIA SLAVIC, AND LUCIO MARCENARO 13.1 Introduction 450 13.2 Base concepts and state of the art 451 13.3 Framework for computing anomaly in self-aware systems 458 13.4 Case study results: anomaly detection on multisensory data from a self-aware vehicle 467 13.5 Conclusions 476 References 477 14. Deep plug-and-play and deep unfolding methods for image restoration KAI ZHANG AND RADU TIMOFTE 14.1 Introduction 481 14.2 Half quadratic splitting (HQS) algorithm 484 14.3 Deep plug-and-play image restoration 485 14.4 Deep unfolding image restoration 492 14.5 Experiments 495 14.6 Discussion and conclusions 504 References 505 15. Visual adversarial attacks and defenses CHANGJAE OH, ALESSIO XOMPERO, AND ANDREA CAVALLARO 15.1 Introduction 511 15.2 Problem definition 512 15.3 Properties of an adversarial attack 514 15.4 Types of perturbations 515 15.5 Attack scenarios 515 15.6 Image processing 522 15.7 Image classification 523 15.8 Semantic segmentation and object detection 529 15.9 Object tracking 529 15.10 Video classification 531 15.11 Defenses against adversarial attacks 533 15.12 Conclusions 537 References 538 Index 545

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Book SynopsisLearn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book DescriptionDeep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is forThis book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.Table of ContentsTable of Contents Getting Started Image Classification Image Retrieval Object Detection Semantic Segmentation Similarity Learning Image Captioning Generative models Video Classification Deployment

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Book SynopsisThis book focuses on the use of AI/ML-based techniques to solve issues related to IoT-based environments, as well as their applications. It addresses, among others, signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user/application behavior prediction, software-defi ned networking, congestion control, communication network optimization, security, and anomaly detection.

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Book SynopsisThis book provides research on the state-of-the-art methods for data management in the fourth industrial revolution, with particular focus on cloud.based data analytics for digital manufacturing infrastructures. Innovative techniques and methods for secure, flexible and profi table cloud manufacturing will be gathered to present advanced and specialized research in the selected area.

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Book SynopsisAugmented and Virtual Reality are revolutionizing present and future technologies: these are the fastest growing and most fascinating areas of technologies at present. This book aims to provide insight into the theory and applications of Augmented and Virtual Reality to multiple technologies such as IoT (Internet of Things), ML (Machine Learning), AI (Artifi cial Intelligence), Healthcare and Education.

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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.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book Synopsis1. One Instruction Set Computing.- 1.1 What is One Instruction Set Computing?.- 1.2 Why Study OISC?.- 1.3 A Look Ahead.- 1.4 Exercises.- 2 Instruction Sets.- 2.1 Elements of an Instruction.- 2.2 Operands.- 2.3 Instruction Formats.- 2.4 Core Set of Instructions.- 2.5 Addressing Modes.- 2.6 Exercises.- 3 Types of Computer Architectures.- 3.1 Overview.- 3.2 A Simple Taxonomy.- 3.3 Accumulator.- 3.4 Register-Memory.- 3.5 Register-Oriented.- 3.6 Exercises.- 4 Evolution of Instruction Sets.- 4.1 Motivation.- 4.2 Evolution of Microprocessors.- 4.3 Timeline.- 4.4 Exercises.- 5 CISC, RISC, OISC.- 5.1 CISC versus RISC.- 5.2 Is OISC a CISC or RISC?.- 5.3 Processor Complexity.- 5.4 Exercises.- 6 OISC Architectures.- 6.1 Single Instruction Types.- 6.2 MOVE.- 6.3 Comparing OISC Models.- 6.4 Variants of SBN and MOVE.- 6.5 OISC Continuum.- 6.6 Exercises.- 7 Historical Review of OISC.- 7.1 Subtract and Branch if Negative (SBN).- 7.2 MOVE-based.- 7.3 Timeline.- 7.4 Exercises.- 8 Instruction Set Completeness.- 8.1 Instruction Set Completeness.- 8.2 A Practical Approach to Determining Completeness.- 8.3 Completeness of Two OISCs.- 8.4 Exercises.- 9 OISC Mappings.- 9.1 Mapping OISC to Conventional Architectures.- 9.2 Synthesizing Instructions.- 9.3 Code Fragments.- 9.4 Implementing OISC using OISC.- 9.5 Exercises.- 10 Parallel Architectures.- 10.1 Von Neumann Bottleneck.- 10.2 Parallel Processing.- 10.3 Flynn's Taxonomy for Parallelism.- 10.4 Exercises.- 11 Applications and Implementations.- 11.1 OlSC-like Phenomena.- 11.2 Field Programmable Gate Arrays.- 11.3 Applications.- 11.4 Image Processing.- 11.5 Future Work with OISC.- 11.6 Exercises.- Appendix A: A Generic Microprocessor and OISC.- Appendix B: One Instruction Set Computer Implementation.- B.1 6502 Opcodes Summary.- B.2 6502Opcodes Mapped to MOVE OISC.- B.3 6502 Addressing as MOVE-based OISC.- B.4 6502 Addressing Modes and MOVE-based OISC.- Appendix C: Dilation Code Implementation.- Appendix D: Compiler Output for Dilation.- Appendix E: OISC Equivalent of Dilation.- References.- About the Authors.Trade Review`This book gives a fine introduction to basic computer architecture. A few years ago, this book would have interested only graduate computer science and engineering students. These days, some high school students even create Linux clusters, and interest in it may be even more widespread.' R.P. Sarna, Maine Maritime Academy in Choice, December 2003Table of ContentsPreface. Acknowledgements. - 1: One Instruction Set Computing. 1.1. What is One Instruction Set Computing? 1.2. Why Study OISC? 1.3. A Look Ahead. 1.4. Exercises. 2: Instruction Sets. 2.1. Elements of an Instruction. 2.2. Operands. 2.3. Instruction Formats. 2.4. Core Set of Instructions. 2.5. Addressing Modes. 2.6. Exercises. - 3: Types of Computer Architecture. 3.1. Overview. 3.2.A Simple Taxonomy. 3.3. Accumulator. 3.4. Register-Memory. 3.5. Register-Oriented. 3.6. Exercises. - 4: Evolution of Instruction Sets. 4.1. Motivation. 4.2. Evolution of Microprocessors. 4.3. Timeline. 4.4. Exercises. - 5: CISC, RISC, OISC. 5.1. CISC versus RISC. 5.2. Is OISC a CISC or a RISC? 5.3. Processor Complexity. 5.4. Exercises. - 6: OISC Architectures. 6.1. Single Instruction Types. 6.2. MOVE. 6.3. Comparing OISC Models. 6.4. Variants of SBN and MOVE. 6.5. OISC Continuum. 6.6. Exercises. - 7: Historical Review of OISC. 7.1. Subtract and Branch if Negative (SBN). 7.2. MOVE-Based. 7.3. Timeline. 7.4. Exercises. - 8: Instruction Set Completeness. 8.1. Instruction Set Completeness. 8.2. A Practical Approach to Determining Completeness. 8.3. Completeness of Two OISCs. 8.4. Exercises. - 9: OISC Mappings. 9.1. Mapping OISC to Conventional Architectures. 9.2. Synthesizing Instructions. 9.3. Code Fragments. 9.4. Implementing OISC Using OISC. 9.5. Exercises. - 10: Parallel Architectures. 10.1. Von Neumann Bottleneck. 10.2. Parallel Processing. 10.3. Flynn's Taxonomy for Parallelism. 10.4. Exercises. - 11: Applications and Implementations. 11.1. OISC-Like Phenomena. 11.2. Field Programmable Gate Arrays. 11.3. Applications. 11.4. Image Processing. 11.5. Future Work with OISC. 11.6. Exercises. - Appendix A: A Generic Microprocessor and OISC. - Appendix B: One Instruction Set Computer Implementation. - Appendix C: Dilation Code Implementation. - Appendix D: Compiler Output for Dilation. - Appendix E: OISC Equivalent of Dilation. Glossary. References. Index. About the Authors.

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Book Synopsisto Computer Algebra.- Algebra of Polynomials, Rational Functions, and Power Series.- Normal Forms and Algebraic Representations.- Arithmetic of Polynomials, Rational Functions, and Power Series.- Homomorphisms and Chinese Remainder Algorithms.- Newton's Iteration and the Hensel Construction.- Polynomial GCD Computation.- Polynomial Factorization.- Solving Systems of Equations.- Gröbner Bases for Polynomial Ideals.- Integration of Rational Functions.- The Risch Integration Algorithm.Trade Review`The Computer Algebra community has been waiting for years for this book to appear. ...the book is a masterpiece and can be recommended to everyone interested in the algorithms for computer algebra, either as a reference for further research or just to give the casual user an idea why things work as good (or as bad) as they do in computer algebra packages. ...the recommendation would be clear: Buy this book! As it stands now my only advice is to stay away from this book, because you might be tempted to buy it anyway (at least I was).' Computer Algebra Nederland Newsletter, June 1993 Table of ContentsPreface. 1. Introduction to Computer Algebra. 2. Algebra of Polynomials, Rational Functions, and Power Series. 3. Normal Forms and Algebraic Representations. 4. Arithmetic of Polynomial, Rational Functions, and Power Series. 5. Homomorphisms and Chinese Remainder Algorithms. 6. Newton's Iteration and the Hensel Construction. 7. Polynomials GCD Computation. 8. Polynomial Factorization. 9. Solving Systems of Equation. 10. Grobner Bases for Polynomial Ideals. 11. Integration of Rational Functions. 12. The Risch Integration Algorithm. Notation. Index.

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Book SynopsisDevelop scalable computer vision solutions for real-world business problems and discover scaling, cost reduction, security, and bias mitigation best practices with AWS AI/ML servicesPurchase of the print or Kindle book includes a free PDF eBookKey Features Learn how to quickly deploy and automate end-to-end CV pipelines on AWS Implement design principles to mitigate bias and scale production of CV workloads Work with code examples to master CV concepts using AWS AI/ML services Book DescriptionComputer vision (CV) is a field of artificial intelligence that helps transform visual data into actionable insights to solve a wide range of business challenges. This book provides prescriptive guidance to anyone looking to learn how to approach CV problems for quickly building and deploying production-ready models.You'll begin by exploring the applications of CV and the features of Amazon Rekognition and Amazon Lookout for Vision. The book will then walk you through real-world use cases such as identity verification, real-time video analysis, content moderation, and detecting manufacturing defects that'll enable you to understand how to implement AWS AI/ML services. As you make progress, you'll also use Amazon SageMaker for data annotation, training, and deploying CV models. In the concluding chapters, you'll work with practical code examples, and discover best practices and design principles for scaling, reducing cost, improving the security posture, and mitigating bias of CV workloads.By the end of this AWS book, you'll be able to accelerate your business outcomes by building and implementing CV into your production environments with the help of AWS AI/ML services.What you will learn Apply CV across industries, including e-commerce, logistics, and media Build custom image classifiers with Amazon Rekognition Custom Labels Create automated end-to-end CV workflows on AWS Detect product defects on edge devices using Amazon Lookout for Vision Build, deploy, and monitor CV models using Amazon SageMaker Discover best practices for designing and evaluating CV workloads Develop an AI governance strategy across the entire machine learning life cycle Who this book is forIf you are a machine learning engineer or data scientist looking to discover best practices and learn how to build comprehensive CV solutions on AWS, this book is for you. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.Table of ContentsTable of Contents Product Information Document Computer Vision Applications and AWS AI/ML Overview Interacting with Amazon Rekognition Creating Custom Models with Amazon Rekognition Custom Labels Using Identity Verification to Build a Contactless Hotel Check-In System Automating a Video Analysis Pipeline Moderating Content with AWS AI Services Introducing Amazon Lookout for Vision Detecting Manufacturing Defects using CV at the Edge Labeling Data with Amazon SageMaker Ground Truth Using Amazon SageMaker for Computer Vision Integrating Human-in-the-Loop with Amazon Augmented AI (A2I) Best Practices for Designing an End-to-End CV Pipeline Applying AI Governance in CV

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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

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Book SynopsisThis easy-to-follow textbook presents an engaging introduction to the fascinating world of medical image analysis. Avoiding an overly mathematical treatment, the text focuses on intuitive explanations, illustrating the key algorithms and concepts in a way which will make sense to students from a broad range of different backgrounds.Topics and features: explains what light is, and how it can be captured by a camera and converted into an image, as well as how images can be compressed and stored; describes basic image manipulation methods for understanding and improving image quality, and a useful segmentation algorithm; reviews the basic image processing methods for segmenting or enhancing certain features in an image, with a focus on morphology methods for binary images; examines how to detect, describe, and recognize objects in an image, and how the nature of color can be used for segmenting objects; introduces a statistical method to determine what class of object the pixels in an image represent; describes how to change the geometry within an image, how to align two images so that they are as similar as possible, and how to detect lines and paths in images; provides further exercises and other supplementary material at an associated website.This concise and accessible textbook will be invaluable to undergraduate students of computer science, engineering, medicine, and any multi-disciplinary courses that combine topics on health with data science. Medical practitioners working with medical imaging devices will also appreciate this easy-to-understand explanation of the technology.Table of ContentsIntroduction Image Acquisition Image Storage and Compression Point Processing Neighborhood Processing Morphology BLOB Analysis Color Images Pixel Classification Geometric Transformations Image Registration Line and Path Detection Appendix A: Bits, Bytes and Binary Numbers Appendix B: Mathematical Definitions

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Book SynopsisThis textbook is designed for postgraduate studies in the field of 3D Computer Vision. It also provides a useful reference for industrial practitioners; for example, in the areas of 3D data capture, computer-aided geometric modelling and industrial quality assurance. This second edition is a significant upgrade of existing topics with novel findings. Additionally, it has new material covering consumer-grade RGB-D cameras, 3D morphable models, deep learning on 3D datasets, as well as new applications in the 3D digitization of cultural heritage and the 3D phenotyping of crops. Overall, the book covers three main areas: ● 3D imaging, including passive 3D imaging, active triangulation 3D imaging, active time-of-flight 3D imaging, consumer RGB-D cameras, and 3D data representation and visualisation; ● 3D shape analysis, including local descriptors, registration, matching, 3D morphable models, and deep learning on 3D datasets; and ● 3D applications, including 3D face recognition, cultural heritage and 3D phenotyping of plants. 3D computer vision is a rapidly advancing area in computer science. There are many real-world applications that demand high-performance 3D imaging and analysis and, as a result, many new techniques and commercial products have been developed. However, many challenges remain on how to analyse the captured data in a way that is sufficiently fast, robust and accurate for the application. Such challenges include metrology, semantic segmentation, classification and recognition. Thus, 3D imaging, analysis and their applications remain a highly-active research field that will continue to attract intensive attention from the research community with the ultimate goal of fully automating the 3D data capture, analysis and inference pipeline. Table of ContentsIntroduction.- Part I 3D Shape Acquisition, Representation and Visualization.- Passive 3D Imaging.- Active-triangulation 3D Imaging Systems for Industrial Inspection.- Active Time-of-Flight 3D Imaging Systems for Medium-range Applications.- Consumer-grade RGB-D cameras.- 3D Data Representation, Storage and Processing.- Part II: 3D Shape Analysis and Inference.- 3D Local Descriptors -- from Hand-crafted to Learned.- 3D Shape Registration.- 3D Shape Matching for Retrieval and Recognition.- 3D Morphable Models: the Face, Ear and Head.- Deep Learning on 3D Data.- Part III: 3D Imaging Applications.- 3D Face Recognition.- 3D Digitization of Cultural Heritage.- 3D Phenotyping of Plants.- Index

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Book SynopsisPREVIOUS EDITIONThis textbook introduces the “Fundamentals of Multimedia”, addressing real issues commonly faced in the workplace. The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia. Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies. Features: presents an overview of the key concepts in multimedia, including color science; reviews lossless and lossy compression methods for image, video and audio data; examines the demands placed by multimedia communications on wired and wireless networks; discusses the impact of social media and cloud computing on information sharing and on multimedia content search and retrieval; includes study exercises at the end of each chapter; provides supplementary resources for both students and instructors at an associated website.Table of ContentsPart I: Introduction and Multimedia Data Representations.- Introduction to Multimedia.- A Taste of Multimedia.- Graphics and Image Data Representations.- Color in Image and Video.- Fundamental Concepts in Video.- Basics of Digital Audio.- Part II: Multimedia Data Compression.- Lossless Compression Algorithms.- Lossy Compression Algorithms.- Image Compression Standards.- Basic Video Compression Techniques.- MPEG Video Coding: MPEG-1, 2, 4 and 7.- Modern Video Coding Standards: H.264, H.265, and H.266.- Basic Audio Compression Techniques.- MPEG Audio Compression.- Part III: Multimedia Communications and Networking.- Network Services and Protocols for Multimedia Communications.- Internet Multimedia Content Distribution.- Multimedia over Wireless and Mobile Networks.- Cloud Computing for Multimedia Services.- Part IV: Human-Centric Interactive Multimedia.- Online Social Media Sharing.- Augmented Reality and Virtual Reality.- Content-Based Retrieval in Digital Libraries.- Cloud Computing for Multimedia Services.

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