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9780521887380

Object Categorization: Computer and Human Vision Perspectives

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  • ISBN13:

    9780521887380

  • ISBN10:

    0521887380

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2009-09-07
  • Publisher: Cambridge University Press

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Summary

This edited volume presents a unique multidisciplinary perspective on the problem of visual object categorization. The result of a series of four highly successful workshops on the topic, the book gathers many of the most distinguished researchers from both computer and human vision to reflect on their experience, identify open problems, and foster a cross-disciplinary discussion with the idea that parallel problems and solutions have arisen in both domains. Twenty-seven of these workshop speakers have contributed chapters, including fourteen from computer vision and thirteen from human vision. Their contributions range from broad perspectives on the problem to more specific approaches, collectively providing important historical context, identifying the major challenges, and presenting recent research results. This multidisciplinary collection is the first of its kind on the topic of object categorization, providing an outstanding context for graduate students and researchers in both computer and human vision.

Author Biography

Sven J. Dickinson is Professor of Computer Science at the University of Toronto. From 1994 until 2000, he was Assistant Professor at Rutgers University, where he held joint appointments in the Department of Computer Science and the Rutgers Center for Cognitive Science. He was cochair of the 1997, 1999, 2004, and 2007 IEEE International Workshops on Generic Object Recognition and Categorization and cochair of the First International Workshop on Shape on Shape Perception in Human and Computer Vision in 2008. Alescheck; Leonardis is a Full Professor and Head of the Visual Cognitive Systems Laboratory at the University of Ljubljana and Adjunct Professor at the Faculty of Computer Science, Graz University of Technology. He was a researcher and visiting professor at the University of Pennsylvania, Vienna University of Technology, Swiss Federal Institute of Technology, and University of Erlangen. Bernt Schiele is Full Professor of Computer Science at TU Darmstadt, Germany. He obtained his Ph.D. from INPG in Grenoble, France. He was a researcher at Carnegie Mellon University and visiting Assistant Professor at Massachusetts Institute of Technology, as well as Assistant Professor at the Swiss Federal Institute of Technology in Zurich (ETH Zurich). Michael J. Tarr is the Co-Director of the Center for the Neural Basis of Cognition and a Professor of Psychology at Carnegie Mellon University. From 1989 to 1995, he was an Assistant Professor of Psychology and Computer Science at Yale University. From 1995 to 2009, he was a Professor of Cognitive and Linguistic Sciences at Brown University. While at Brown he also served as the Co-Director of the Center for Vision Research.

Table of Contents

Prefacep. ix
Contributorsp. xiii
The Evolution of Object Categorization and the Challenge of Image Abstractionp. 1
A Strategy for Understanding How the Brain Accomplishes Object Recognitionp. 38
Visual Recognition Circa 2008p. 55
On What It Means to See, and What We Can Do About Itp. 69
Generic Object Recognition by Inference of 3-D Volumetric Partsp. 87
What Has fMRI Taught Us About Object Recognition?p. 102
Object Recognition Through Reasoning About Functionality: A Survey of Related Workp. 129
The Interface Theory of Perception: Natural Selection Drives True Perception to Swift Extinctionp. 148
Words and Pictures: Categories, Modifiers, Depiction, and Iconographyp. 167
Structural Representation of Object Shape in the Brainp. 182
Learning Hierarchical Compositional Representations of Object Structurep. 196
Object Categorization in Man, Monkey, and Machine: Some Answers and Some Open Questionsp. 216
Learning Compositional Models for Object Categories from Small Sample Setsp. 241
The Neurophysiology and Computational Mechanisms of Object Representationp. 257
From Classification to Full Object Interpretationp. 288
Visual Object Discoveryp. 301
Towards Integration of Different Paradigms in Modeling, Representation, and Learning of Visual Categoriesp. 324
Acquisition and Disruption of Category Specificity in the Ventral Visual Stream: The Case of Late Developing and Vulnerable Face-Related Cortexp. 348
Using Simple Features and Relationsp. 369
The Proactive Brain: Using Memory-Based Predictions in Visual Recognitionp. 384
Spatial Pyramid Matchingp. 401
Visual Learning for Optimal Decisions in the Human Brainp. 416
Shapes and Shock Graphs: From Segmented Shapes to Shapes Embedded in Imagesp. 430
Neural Encoding of Scene Statistics for Surface and Object Inferencep. 451
Medial Models for Visionp. 475
Multimodal Categorizationp. 488
Comparing 2-D Images of 3-D Objectsp. 502
Indexp. 517
Table of Contents provided by Ingram. All Rights Reserved.

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