Computational Models for Cognitive Vision

**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. 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. "This book focuses on computing aspects and shows how the cognitive principles borrowed from other disciplines can be used to solve problems of computer vision. The book begins with an introduction to the problems of computer vision and why cognitive principles should be used to solve these problems. Then it moves on to a discussion of deductive, inductive, and abductive reasoning and the psychological observations and deductions of various theories and models of cognitive vision. Other topics covered include knowledge representation techniques, evolution of cognitive architectures, machine learning approaches, and more. The book ends with salient observations and future research directions"-- Provided by publisher Cover Computational Models for Cognitive Vision Copyright Contents About the Author Acknowledgments Preface Acronyms 1 Introduction 2 Early Vision 3 Bayesian Reasoning for Perception and Cognition 4 Late Vision 5 Visual Attention 6 Cognitive Architectures 7 Knowledge Representation for Cognitive Vision 8 Deep Learning for Visual Cognition 9 Applications of Visual Cognition 10 Conclusion References Index