Red/green filters included in plastic pocket on p.  of cover.
In this accessible and engaging introduction to modern vision science, James Stone uses visual illusions to explore how the brain sees the world. Understanding vision, Stone argues, is not simply a question of knowing which neurons respond to particular visual features, but also requires a computational theory of vision. Stone draws together results from David Marr's computational framework, Barlow's efficient coding hypothesis, Bayesian inference, Shannon's information theory, and signal processing to construct a coherent account of vision that explains not only how the brain is fooled by particular visual illusions, but also why any biological or computer vision system should also be fooled by these illusions.This short text includes chapters on the eye and its evolution, how and why visual neurons from different species encode the retinal image in the same way, how information theory explains color aftereffects, how different visual cues provide depth information, how the imperfect visual information received by the eye and brain can be rescued by Bayesian inference, how different brain regions process visual information, and the bizarre perceptual consequences that result from damage to these brain regions. The tutorial style emphasizes key conceptual insights, rather than mathematical details, making the book accessible to the nonscientist and suitable for undergraduate or postgraduate study.
This text provides an introduction to computational aspects of early vision, in particular, color, stereo, and visual navigation. It integrates approaches from psychophysics and quantitative neurobiology, as well as theories and algorithms from machine vision and photogrammetry. When presenting mathematical material, it uses detailed verbal descriptions and illustrations to clarify complex points. The text is suitable for upper-level students in neuroscience, biology, and psychology who have basic mathematical skills and are interested in studying the mathematical modeling of perception.
This book revolutionizes how vision can be taught to undergraduate and graduate students in cognitive science, psychology, and optometry. It is the first comprehensive textbook on vision to reflect the integrated computational approach of modern research scientists. This new interdisciplinary approach, called "vision science," integrates psychological, computational, and neuroscientific perspectives. The book covers all major topics related to vision, from early neural processing of image structure in the retina to high-level visual attention, memory, imagery, and awareness. The presentation throughout is theoretically sophisticated yet requires minimal knowledge of mathematics. There is also an extensive glossary, as well as appendices on psychophysical methods, connectionist modeling, and color technology. The book will serve not only as a comprehensive textbook on vision, but also as a valuable reference for researchers in cognitive science, psychology, neuroscience, computer science, optometry, and philosophy.
Understanding Vision explains the computational principles and models of biological visual processing, and in particular, of primate vision. The book is written in such a way that vision scientists, unfamiliar with mathematical details, should be able to conceptually follow the theoretical principles and their relationship with physiological, anatomical, and psychological observations, without going through the more mathematical pages. For those with a physical science background, especially those from machine vision, this book serves as an analytical introduction to biological vision. It can be used as a textbook or a reference book in a vision course, or a computational neuroscience course for graduate students or advanced undergraduate students. It is also suitable for self-learning by motivated readers. Understanding Vision is valuable for students and researchers in computational neuroscience, vision science, machine and computer vision, as well as physicists interested in visual processes.
How we see and how we visualize: why the scientific account differs from our experience.
The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks -- which do not reflect several important features of the ventral stream architecture and physiology -- have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex.
Aims and Scope This book is both an introductory textbook and a research monograph on modeling the statistical structure of natural images. In very simple terms, “natural images” are photographs of the typical environment where we live. In this book, their statistical structure is described using a number of statistical models whose parameters are estimated from image samples. Our main motivation for exploring natural image statistics is computational m- eling of biological visual systems. A theoretical framework which is gaining more and more support considers the properties of the visual system to be re?ections of the statistical structure of natural images because of evolutionary adaptation processes. Another motivation for natural image statistics research is in computer science and engineering, where it helps in development of better image processing and computer vision methods. While research on natural image statistics has been growing rapidly since the mid-1990s, no attempt has been made to cover the ?eld in a single book, providing a uni?ed view of the different models and approaches. This book attempts to do just that. Furthermore, our aim is to provide an accessible introduction to the ?eld for students in related disciplines.
An essential reference book for visual science.
The derivation, exposition, and justification of the Selective Tuning model of vision and attention.
In recent years there has been a host of new advances in our understanding of how we see. From molecular genetics come details of the photopigments and the molecular causes of disorders like colour blindness. In-depth analysis has shown how a cell converts light into a neural signal using the photopigments. Traditional techniques of microelectrode recording along with new techniques of functional imaging - such as PET scans - have made it possible to understand how visual information is processed in the brain. This processing results in the single coherent perception of the world we see in our 'mind's eye'. An Introduction to the Visual System provides a concise, but detailed, overview of this field. It is clearly written, and each chapter ends with a helpful 'key points' section. It is ideal for anyone studying visual perception, from the second year of an undergraduate course onwards.
Introducing the fundamental issues in psycholinguistics, this book explores the amazing story of the unconscious processes that take place when humans use language. It is an ideal text for undergraduates taking a first course in the study of language. Topics covered include the biological foundations of language; acquisition of first and second languages in children and adults; the mental lexicon; and speech production, perception, and processing Structured as an engaging narrative that takes the reader from an idea in the mind of a speaker to its comprehension in the mind of the hearer Reflects the latest empirical developments in psycholinguistics, and is illustrated throughout with examples from bilingual as well as monolingual language processing, second language acquisition, and sign languages Student-friendly features include chapter-by-chapter study questions and discussion summaries; the appendix offers an excellent overview of experimental designs in psycholinguistics, and prepares students for their own research Written by an internationally-regarded author team, drawing on forty years of experience in teaching psycholinguistics
This comprehensively updated and expanded revision of the successful second edition continues to provide detailed coverage of the ever-growing range of research topics in vision. In Part I, the treatment of visual physiology has been extensively revised with an updated account of retinal processing, a new section explaining the principles of spatial and temporal filtering which underlie discussions in later chapters, and an up-to-date account of the primate visual pathway. Part II contains four largely new chapters which cover recent psychophysical evidence and computational model of early vision: edge detection, perceptual grouping, depth perception, and motion perception. The models discussed are extensively integrated with physiological evidence. All other chapters in Parts II, III, and IV have also been thoroughly updated.
"In this book, Andy Baxevanis and Francis Ouellette . . . have undertaken the difficult task of organizing the knowledge in this field in a logical progression and presenting it in a digestible form. And they have done an excellent job. This fine text will make a major impact on biological research and, in turn, on progress in biomedicine. We are all in their debt." —Eric Lander from the Foreword Reviews from the First Edition "...provides a broad overview of the basic tools for sequence analysis ... For biologists approaching this subject for the first time, it will be a very useful handbook to keep on the shelf after the first reading, close to the computer." —Nature Structural Biology "...should be in the personal library of any biologist who uses the Internet for the analysis of DNA and protein sequence data." —Science "...a wonderful primer designed to navigate the novice through the intricacies of in scripto analysis ... The accomplished gene searcher will also find this book a useful addition to their library ... an excellent reference to the principles of bioinformatics." —Trends in Biochemical Sciences This new edition of the highly successful Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins provides a sound foundation of basic concepts, with practical discussions and comparisons of both computational tools and databases relevant to biological research. Equipping biologists with the modern tools necessary to solve practical problems in sequence data analysis, the Second Edition covers the broad spectrum of topics in bioinformatics, ranging from Internet concepts to predictive algorithms used on sequence, structure, and expression data. With chapters written by experts in the field, this up-to-date reference thoroughly covers vital concepts and is appropriate for both the novice and the experienced practitioner. Written in clear, simple language, the book is accessible to users without an advanced mathematical or computer science background. This new edition includes: All new end-of-chapter Web resources, bibliographies, and problem sets Accompanying Web site containing the answers to the problems, as well as links to relevant Web resources New coverage of comparative genomics, large-scale genome analysis, sequence assembly, and expressed sequence tags A glossary of commonly used terms in bioinformatics and genomics Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition is essential reading for researchers, instructors, and students of all levels in molecular biology and bioinformatics, as well as for investigators involved in genomics, positional cloning, clinical research, and computational biology.
"This volume contains succinct, yet clear, descriptions of each of these topics and is best suited for readers who are delving into these concepts for the first time." -The Quarterly Review of Biology, 2009 This textbook provides an ideal introduction to computational cell biology for students of biology and bioinformatics. In particular the text focuses on a network-based approach to the study of cellular systems. Almost 30 carefully designed study exercises offer excellent support for those preparing for exams in these subjects, and help introduce the more technical aspects of the topic while keeping maths to a minimum.
More than one third of the human brain is devoted to the processes of seeing - vision is after all the main way in which we gather information about the world. But human vision is a dynamic process during which the eyes continually sample the environment. Where most books on vision consider it as a passive activity, this book is unique in focusing on vision as an 'active' process. It goes beyond most accounts of vision where the focus is on seeing, to provide an integrated account of seeing AND looking. The book starts by pointing out the weaknesses in our traditional approaches to vision and the reason we need this new approach. It then gives a thorough description of basic details of the visual and oculomotor systems necessary to understand active vision. The book goes on to show how this approach can give a new perspective on visual attention, and how the approach has progressed in the areas of visual orienting, reading, visual search, scene perception and neuropsychology. Finally, the book summarises progress by showing how this approach sheds new light on the old problem of how we maintain perception of a stable visual world. Written by two leading vision scientists, this book will be valuable for vision researchers and psychology students, from undergraduate level upwards.
Written with the advanced undergraduate in mind, this book introduces into the field of Bioinformatics. The authors explain the computational and conceptional background to the analysis of large-scale sequence data. Many of the corresponding analysis methods are rooted in evolutionary thinking, which serves as a common thread throughout the book. The focus is on methods of comparative genomics and subjects covered include: alignments, gene finding, phylogeny, and the analysis of single nucleotide polymorphisms (SNPs). The volume contains exercises, questions & answers to selected problems.
A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab computer code examples.
Computational approaches dominate contemporary cognitive science, promising a unified, scientific explanation of how the mind works. However, computational approaches raise major philosophical and scientific questions. In what sense is the mind computational? How do computational approaches explain perception, learning, and decision making? What kinds of challenges should computational approaches overcome to advance our understanding of mind, brain, and behaviour? The Routledge Handbook of the Computational Mind is an outstanding overview and exploration of these issues and the first philosophical collection of its kind. Comprising thirty-five chapters by an international team of contributors from different disciplines, the Handbook is organised into four parts: History and future prospects of computational approaches Types of computational approach Foundations and challenges of computational approaches Applications to specific parts of psychology. Essential reading for students and researchers in philosophy of mind, philosophy of psychology, and philosophy of science, The Routledge Handbook of the Computational Mind will also be of interest to those studying computational models in related subjects such as psychology, neuroscience, and computer science.