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A computational perspective on visual attention [electronic resource] /
John K. Tsotsos.
imprint
Cambridge, Mass. : MIT Press, c2011.
description
xvi, 308 p. : ill. (some col.) ; 24 cm.
ISBN
0262015412 (hardcover : alk. paper), 9780262015417 (hardcover : alk. paper)
format(s)
Book
More Details
imprint
Cambridge, Mass. : MIT Press, c2011.
isbn
0262015412 (hardcover : alk. paper)
9780262015417 (hardcover : alk. paper)
restrictions
Licensed for access by U. of T. users.
contents note
Attention: we all know what it is -- Computational foundations --Theories and models of visual attention -- Selective tuning: overview -- Selective tuning: formulation -- Attention, recognition, and binding -- Selective tuning: examples and performance -- Explanations and predictions -- Wrapping up the loose ends.
catalogue key
11948216
 
Includes bibliographical references and indexes.
A Look Inside
About the Author
Author Affiliation
John K. Tsotsos is Professor of Computer Science and Engineering, Distinguished Research Professor of Vision Science, and Canada Research Chair in Computational Vision at York University and a Fellow of the Royal Society of Canada (FRSC).
Reviews
Review Quotes
John Tsotsos manages to achieve the difficult task of striking a good balance betweenan extensive overview and greater depth given the interdisciplinary nature of the theme. This bookis a must read for Ph.D students who are working in this area, as it explicitly states several opentopics of research and even offers overt hints and methods to address them.
"John Tsotsos manages to achieve the difficult task of striking a good balance between an extensive overview and greater depth given the interdisciplinary nature of the theme. This book is a must read for Ph.D students who are working in this area, as it explicitly states several open topics of research and even offers overt hints and methods to address them." -- Cognitive Systems Research
The algorithmic architecture of Tsotsos' updated selective tuning model will haveimmediate appeal for computational neuroscientists....
"The algorithmic architecture of Tsotsos' updated selective tuning model will have immediate appeal for computational neuroscientists..." -- Thilo Womelsdorf , Trends in Cognitive Sciences
In addition to a giving us comprehensive presentation of John Tsotsos's importanttheory of visual attention, this book provides an excellent grounding in the fundamental issues, asseen from a rigorous, computational point of view.
"In addition to a giving us comprehensive presentation of John Tsotsos's important theory of visual attention, this book provides an excellent grounding in the fundamental issues, as seen from a rigorous, computational point of view." Jeremy M. Wolfe , Professor of Ophthalmology and Radiology, Harvard Medical School, and Director, Visual Attention Lab, Brigham and Women's Hospital
"This readable and scholarly book offers fresh insights for novices and experts alike. The author's Selective Tuning model of visual attention provides a framework that integrates the various expressions of visual attention and the biology of the visual system grounded in the logic of computation." Jeffrey D. Schall , E. Bronson Ingram Professor of Neuroscience, Vanderbilt University, and Director, Vanderbilt Vision Research Center
To find out how to look for other reviews, please see our guides to finding book reviews in the Sciences or Social Sciences and Humanities.
Summaries
Bowker Data Service Summary
The author offers a comprehensive, up-to-date overview of attention theories and models and a full description of the selective tuning model, confining the formal elements to two chapters and two appendixes.
Main Description
Although William James declared in 1890, "Everyone knows what attention is," today there are many different and sometimes opposing views on the subject. This fragmented theoretical landscape may be because most of the theories and models of attention offer explanations in natural language or in a pictorial manner rather than providing a quantitative and unambiguous statement of the theory. They focus on the manifestations of attention instead of its rationale. In this book, John Tsotsos develops a formal model of visual attention with the goal of providing a theoretical explanation for why humans (and animals) must have the capacity to attend. He takes a unique approach to the theory, using the full breadth of the language of computation--rather than simply the language of mathematics--as the formal means of description. The result, the Selective Tuning model of vision and attention, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. The overarching conclusion is that human vision is based on a general purpose processor that can be dynamically tuned to the task and the scene viewed on a moment-by-moment basis. Tsotsos offers a comprehensive, up-to-date overview of attention theories and models and a full description of the Selective Tuning model, confining the formal elements to two chapters and two appendixes. The text is accompanied by more than 100 illustrations in black and white and col∨ additional color illustrations and movies are available on the book's Web site
Main Description
Although William James declared in 1890, &"Everyone knows what attention is,&" today there are many different and sometimes opposing views on the subject. This fragmented theoretical landscape may be because most of the theories and models of attention offer explanations in natural language or in a pictorial manner rather than providing a quantitative and unambiguous statement of the theory. They focus on the manifestations of attention instead of its rationale. In this book, John Tsotsos develops a formal model of visual attention with the goal of providing a theoretical explanation for why humans (and animals) must have the capacity to attend. He takes a unique approach to the theory, using the full breadth of the language of computation--rather than simply the language of mathematics--as the formal means of description. The result, the Selective Tuning model of vision and attention, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. The overarching conclusion is that human vision is based on a general purpose processor that can be dynamically tuned to the task and the scene viewed on a moment-by-moment basis. Tsotsos offers a comprehensive, up-to-date overview of attention theories and models and a full description of the Selective Tuning model, confining the formal elements to two chapters and two appendixes. The text is accompanied by more than 100 illustrations in black and white and col∨ additional color illustrations and movies are available on the book&'s Web site
Main Description
Although William James declared in 1890, "Everyone knows what attention is," today there are many different and sometimes opposing views on the subject. This fragmented theoretical landscape may be because most of the theories and models of attention offer explanations in natural language or in a pictorial manner rather than providing a quantitative and unambiguous statement of the theory. They focus on the manifestations of attention instead of its rationale. In this book, John Tsotsos develops a formal model of visual attention with the goal of providing a theoretical explanation for why humans (and animals) must have the capacity to attend. He takes a unique approach to the theory, using the full breadth of the language of computation--rather than simply the language of mathematics--as the formal means of description. The result, the Selective Tuning model of vision and attention, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. The overarching conclusion is that human vision is based on a general purpose processor that can be dynamically tuned to the task and the scene viewed on a moment-by-moment basis. Tsotsos offers a comprehensive, up-to-date overview of attention theories and models and a full description of the Selective Tuning model, confining the formal elements to two chapters and two appendixes. The text is accompanied by more than 100 illustrations in black and white and color; additional color illustrations and movies are available on the book's Web site
Table of Contents
Prefacep. xi
Acknowledgmentsp. xv
Attention-We All Know What It Isp. 1
But Do We Really?p. 1
Moving Toward a Computational Viewpointp. 7
What Is Attention?p. 10
Computational Foundationsp. 11
Attempting to Understand Visual Processing Capacityp. 11
The Language of Computationp. 14
Capacity Limits and Computational Complexityp. 16
Human Perception/Cognition and Computationp. 18
The Computational Complexity of Visionp. 21
Extending to Active Visionp. 29
Extending to Cognition and Actionp. 32
Extending to Sensor Planningp. 32
Complexity Constrains Visual Processing Architecturep. 33
The Problems with Pyramidsp. 38
Attention Is. …p. 51
Theories and Models of Visual Attentionp. 53
The Elements of Visual Attentionp. 54
A Taxonomy of Modelsp. 59
Other Relevant Ideasp. 75
Summaryp. 78
Selective Tuning: Overviewp. 81
The Basic Modelp. 82
Saliency and Its Role in STp. 86
Selective Tuning with Fixation Controlp. 88
Differences with Other Modelsp. 93
Summaryp. 96
Selective Tuning: Formulationp. 97
Objectivep. 97
Representationsp. 98
Neurons and Circuits for Selective Tuningp. 106
Selectionp. 114
Competition to Represent a Stimulusp. 121
More on Top-Down Tracingp. 122
Inhibition of Returnp. 124
Peripheral Priority Map Computationp. 124
Fixation History Map Maintenancep. 125
Task Guidancep. 126
Comparisons with Other Modelsp. 127
Summaryp. 131
Attention, Recognition, and Bindingp. 133
What Is Recognition?p. 134
What Is Visual Feature Binding?p. 139
Four Binding Processesp. 141
Binding Decision Processp. 145
Putting It All Togetherp. 146
Summaryp. 149
Selective Tuning: Examples and Performancep. 151
P-Lattice Representation of Visual Motion Informationp. 151
Primingp. 153
Results After a Single Feed-Forward Pass (Convergence Binding)p. 160
Results from a Single Feed-Forward Pass Followed by a Single Recurrent Pass (Full Recurrence Binding)p. 164
Attending to Multiple Stimuli (Type I Iterative Recurrence Binding)p. 166
Empirical Performance of Recurrence Binding (Localization)p. 168
Visual Searchp. 174
Type II Iterative Recurrence Bindingp. 186
Saliency and AIMp. 187
Summaryp. 190
Explanations and Predictionsp. 193
Explanationsp. 195
Predictions with Experimental Supportp. 205
Some Supporting Experimentsp. 211
Summaryp. 231
Wrapping Up the Loose Endsp. 233
The Loose Endsp. 236
Vision as Dynamic Tuning of a General-Purpose Processorp. 247
Final Wordsp. 248
Appendixesp. 251
A Few Notes on Some Relevant Aspects of Complexity Theoryp. 251
Proofs of the Complexity of Visual Matchp. 255
The Representation of Visual Motion Processesp. 265
Referencesp. 275
Author Indexp. 297
Subject Indexp. 305
Table of Contents provided by Ingram. All Rights Reserved.

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