Object Categorization: Computer and Human Vision Perspectives

doi:10.1017/CBO9780511635465

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Object Categorization

Object Categorization

Computer and Human Vision Perspectives

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Edited by Sven J. Dickinson, University of Toronto, Aleš Leonardis, University of Ljubljana, Bernt Schiele, Technische Universität, Darmstadt, Germany, Michael J. Tarr, Carnegie Mellon University, Pennsylvania

Publisher: Cambridge University Press
Online publication date: May 2010
Print publication year: 2009
Online ISBN: 9780511635465
DOI: https://doi.org/10.1017/CBO9780511635465

Subjects: Engineering, Computer Science, Computer Graphics, Image Processing and Robotics, Image Processing and Machine Vision

131.00 (GBP)

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Object Categorization
Edited by Sven J. Dickinson, Aleš Leonardis, Bernt Schiele, Michael J. Tarr
Online ISBN: 9780511635465

Book DOI: https://doi.org/10.1017/CBO9780511635465
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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.

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Object Categorization

Computer and Human Vision Perspectives

This Book has been cited by the following publications. This list is generated based on data provided by Crossref.

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Contents

Frontmatter
pp i-iv

Contents
pp v-viii

Preface
pp ix-xii

Contributors
pp xiii-xvi

1 - The Evolution of Object Categorization and the Challenge of Image Abstraction
pp 1-37

2 - A Strategy for Understanding How the Brain Accomplishes Object Recognition
pp 38-54

3 - Visual Recognition Circa 2008
pp 55-68

4 - On What It Means to See, and WhatWe Can Do About It
pp 69-86

5 - Generic Object Recognition by Inference of 3-D Volumetric Parts
pp 87-101

6 - What Has fMRI Taught Us About Object Recognition?
pp 102-128

7 - Object Recognition Through Reasoning About Functionality: A Survey of Related Work
pp 129-147

8 - The Interface Theory of Perception: Natural Selection Drives True Perception to Swift Extinction
pp 148-166

9 - Words and Pictures: Categories, Modifiers, Depiction, and Iconography
pp 167-181

10 - Structural Representation of Object Shape in the Brain
pp 182-195

11 - Learning Hierarchical Compositional Representations of Object Structure
pp 196-215

12 - Object Categorization in Man, Monkey, and Machine: Some Answers and Some Open Questions
pp 216-240

13 - Learning Compositional Models for Object Categories from Small Sample Sets
pp 241-256

14 - The Neurophysiology and Computational Mechanisms of Object Representation
pp 257-287

15 - From Classification to Full Object Interpretation
pp 288-300

16 - Visual Object Discovery
pp 301-323

17 - Towards Integration of Different Paradigms in Modeling, Representation, and Learning of Visual Categories
pp 324-347

18 - Acquisition and Disruption of Category Specificity in the Ventral Visual Stream: The Case of Late Developing and Vulnerable Face-Related Cortex
pp 348-368

19 - Using Simple Features and Relations
pp 369-383

20 - The Proactive Brain: Using Memory-Based Predictions in Visual Recognition
pp 384-400

21 - Spatial Pyramid Matching
pp 401-415

22 - Visual Learning for Optimal Decisions in the Human Brain
pp 416-429

23 - Shapes and Shock Graphs: From Segmented Shapes to Shapes Embedded in Images
pp 430-450

24 - Neural Encoding of Scene Statistics for Surface and Object Inference
pp 451-474

25 - Medial Models for Vision
pp 475-487

26 - Multimodal Categorization
pp 488-501

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Object Categorization

Computer and Human Vision Perspectives

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