Learn how to apply cognitive principles to the problems of computer vision
Computational Models for Cognitive Vision formulates the computational models for the cognitive principles found in biological vision, and applies those models to computer vision tasks. Such principles include perceptual grouping, attention, visual quality and aesthetics, knowledge-based interpretation and learning, to name a few. The author's ultimate goal is to provide a framework for creation of a machine vision system with the capability and versatility of the human vision.
Written by Dr. Hiranmay Ghosh, the book takes readers through the basic principles and the computational models for cognitive vision, Bayesian reasoning for perception and cognition, and other related topics, before establishing the relationship of cognitive vision with the multi-disciplinary field broadly referred to as "artificial intelligence". The principles are illustrated with diverse application examples in computer vision, such as computational photography, digital heritage and social robots. The author concludes with suggestions for future research and salient observations about the state of the field of cognitive vision.
Other topics covered in the book include:
· knowledge representation techniques
· evolution of cognitive architectures
· deep learning approaches for visual cognition
Undergraduate students, graduate students, engineers, and researchers interested in cognitive vision will consider this an indispensable and practical resource in the development and study of computer vision.
Autorentext
HIRANMAY GHOSH, PHD, was a Research Advisor to TATA Consultancy Services and an Adjunct Faculty Member with the National Institute of Technology Karnataka. During his long professional career, he has served several reputed organizations, including CMC, ECIL and C-DOT and TCS. He was an Adjunct Faculty Member with IIT Delhi, and with the National Institute of Technology Karnataka. He is a Senior Member of IEEE, Life Member of IUPRAI, and a Member of ACM.
Klappentext
LEARN HOW TO APPLY COGNITIVE PRINCIPLES TO THE PROBLEMS OF COMPUTER VISION
Computational Models for Cognitive Vision formulates the computational models for the cognitive principles found in biological vision, and applies those models to computer vision tasks. Such principles include perceptual grouping, attention, visual quality and aesthetics assessment, knowledge-based interpretation and learning, to name a few. The ultimate goal of this book is to provide a framework for creation of a machine vision system with the capability and versatility of human vision.
Written by Dr. Hiranmay Ghosh, the book takes readers through the transformation of visual signals that takes place in the eyes, principles of Bayesian reasoning for perception and cognition, and other related topics, before establishing the relationship of cognitive vision with various principles of the multi-disciplinary field broadly referred to as "artificial intelligence". The theories are illustrated with diverse application examples, such as computational photography, digital heritage and social robots. The author concludes with salient observations about the state of the field of cognitive vision and future research directions.
Other topics covered in the book include:
- knowledge representation techniques for cognitive computing
- evolution of cognitive architectures
- deep learning approaches for visual cognition
Undergraduate students, graduate students, engineers, and researchers interested in cognitive vision will find this an indispensable and practical resource in the development and study of computer vision.
Zusammenfassung
Learn how to apply cognitive principles to the problems of computer vision
Computational Models for Cognitive Vision formulates the computational models for the cognitive principles found in biological vision, and applies those models to computer vision tasks. Such principles include perceptual grouping, attention, visual quality and aesthetics, knowledge-based interpretation and learning, to name a few. The author's ultimate goal is to provide a framework for creation of a machine vision system with the capability and versatility of the human vision.
Written by Dr. Hiranmay Ghosh, the book takes readers through the basic principles and the computational models for cognitive vision, Bayesian reasoning for perception and cognition, and other related topics, before establishing the relationship of cognitive vision with the multi-disciplinary field broadly referred to as artificial intelligence. The principles are illustrated with diverse application examples in computer vision, such as computational photography, digital heritage and social robots. The author concludes with suggestions for future research and salient observations about the state of the field of cognitive vision.
Other topics covered in the book include:
· knowledge representation techniques
· evolution of cognitive architectures
· deep learning approaches for visual cognition
Undergraduate students, graduate students, engineers, and researchers interested in cognitive vision will consider this an indispensable and practical resource in the development and study of computer vision.
Inhalt
About the Author ix
Acknowledgments xi
Preface xiii
Acronyms xv
1 Introduction 1
1.1 What Is Cognitive Vision 2
1.2 Computational Approaches for Cognitive Vision 3
1.3 A Brief Review of Human Vision System 4
1.4 Perception and Cognition 6
1.5 Organization of the Book 7
2 Early Vision9
2.1 Feature Integration Theory 9
2.2 Structure of Human Eye 10
2.3 Lateral Inhibition 13
2.4 Convolution: Detection of Edges and Orientations 14
2.5 Color and Texture Perception 17
2.6 Motion Perception 19
2.6.1 Intensity-Based Approach 19
2.6.2 Token-Based Approach 20
2.7 Peripheral Vision 21
2.8 Conclusion 24
3 Bayesian Reasoning for Perception and Cognition 25
3.1 Reasoning Paradigms 26
3.2 Natural Scene Statistics 27
3.3 Bayesian Framework of Reasoning 28
3.4 Bayesian Networks 32
3.5 Dynamic Bayesian Networks 34
3.6 Parameter Estimation 36
3.7 On Complexity of Models and Bayesian Inference 38
3.8 Hierarchical Bayesian Models 39
3.9 Inductive Reasoning with Bayesian Framework 41
3.9.1 Inductive Generalization 41
3.9.2 Taxonomy Learning 45
3.9.3 Feature Selection 46
3.10 Conclusion 47
4 Late Vision 51
4.1 Stereopsis and Depth Perception 51
4.2 Perception of Visual Quality 53
4.3 Perceptual Grouping 55
4.4 ForegroundBackground Separation 59
4.5 Multi-stability 60
4.6 Object Recognition 61
4.6.1 In-Context Object Recognition 62
4.6.2 Synthesis of Bottom-Up and Top-Down Knowledge 64
4.6.3 Hierarchical Modeling 65
4.6.4 One-Shot Learning 66
4.7 Visual Aesthetics 67
4.8 Conclusion 69
5 Visual Attention 71
5.1 Modeling of Visual Attention 72
5.2 Models for Visual Attention 75
5.2.1 Cognitive Models 75
5.2.2 Information-Theoretic Models 77
5.2.3 Bayesian Models 78
5.2.4 Context-Based Models 79
5.2.5 Object-Based Models 81
5.3 Evaluation 82
5.4 Conclusion 84
6 Cognitive Architectures 87
6.1 Cognitive Modeling 88
6.1.1 Paradigms for Modeling Cognition 88
6.1.2 Levels of Abstraction 91
6.2 Desiderata for Cognitive Architectures 9…