Book contents
- Frontmatter
- Contents
- Preface
- Contributors
- 1 The Evolution of Object Categorization and the Challenge of Image Abstraction
- 2 A Strategy for Understanding How the Brain Accomplishes Object Recognition
- 3 Visual Recognition Circa 2008
- 4 On What It Means to See, and WhatWe Can Do About It
- 5 Generic Object Recognition by Inference of 3-D Volumetric Parts
- 6 What Has fMRI Taught Us About Object Recognition?
- 7 Object Recognition Through Reasoning About Functionality: A Survey of Related Work
- 8 The Interface Theory of Perception: Natural Selection Drives True Perception to Swift Extinction
- 9 Words and Pictures: Categories, Modifiers, Depiction, and Iconography
- 10 Structural Representation of Object Shape in the Brain
- 11 Learning Hierarchical Compositional Representations of Object Structure
- 12 Object Categorization in Man, Monkey, and Machine: Some Answers and Some Open Questions
- 13 Learning Compositional Models for Object Categories from Small Sample Sets
- 14 The Neurophysiology and Computational Mechanisms of Object Representation
- 15 From Classification to Full Object Interpretation
- 16 Visual Object Discovery
- 17 Towards Integration of Different Paradigms in Modeling, Representation, and Learning of Visual Categories
- 18 Acquisition and Disruption of Category Specificity in the Ventral Visual Stream: The Case of Late Developing and Vulnerable Face-Related Cortex
- 19 Using Simple Features and Relations
- 20 The Proactive Brain: Using Memory-Based Predictions in Visual Recognition
- 21 Spatial Pyramid Matching
- 22 Visual Learning for Optimal Decisions in the Human Brain
- 23 Shapes and Shock Graphs: From Segmented Shapes to Shapes Embedded in Images
- 24 Neural Encoding of Scene Statistics for Surface and Object Inference
- 25 Medial Models for Vision
- 26 Multimodal Categorization
- 27 Comparing 2-D Images of 3-D Objects
- Index
- Plate section
14 - The Neurophysiology and Computational Mechanisms of Object Representation
Published online by Cambridge University Press: 20 May 2010
- Frontmatter
- Contents
- Preface
- Contributors
- 1 The Evolution of Object Categorization and the Challenge of Image Abstraction
- 2 A Strategy for Understanding How the Brain Accomplishes Object Recognition
- 3 Visual Recognition Circa 2008
- 4 On What It Means to See, and WhatWe Can Do About It
- 5 Generic Object Recognition by Inference of 3-D Volumetric Parts
- 6 What Has fMRI Taught Us About Object Recognition?
- 7 Object Recognition Through Reasoning About Functionality: A Survey of Related Work
- 8 The Interface Theory of Perception: Natural Selection Drives True Perception to Swift Extinction
- 9 Words and Pictures: Categories, Modifiers, Depiction, and Iconography
- 10 Structural Representation of Object Shape in the Brain
- 11 Learning Hierarchical Compositional Representations of Object Structure
- 12 Object Categorization in Man, Monkey, and Machine: Some Answers and Some Open Questions
- 13 Learning Compositional Models for Object Categories from Small Sample Sets
- 14 The Neurophysiology and Computational Mechanisms of Object Representation
- 15 From Classification to Full Object Interpretation
- 16 Visual Object Discovery
- 17 Towards Integration of Different Paradigms in Modeling, Representation, and Learning of Visual Categories
- 18 Acquisition and Disruption of Category Specificity in the Ventral Visual Stream: The Case of Late Developing and Vulnerable Face-Related Cortex
- 19 Using Simple Features and Relations
- 20 The Proactive Brain: Using Memory-Based Predictions in Visual Recognition
- 21 Spatial Pyramid Matching
- 22 Visual Learning for Optimal Decisions in the Human Brain
- 23 Shapes and Shock Graphs: From Segmented Shapes to Shapes Embedded in Images
- 24 Neural Encoding of Scene Statistics for Surface and Object Inference
- 25 Medial Models for Vision
- 26 Multimodal Categorization
- 27 Comparing 2-D Images of 3-D Objects
- Index
- Plate section
Summary
Introduction
A concise description of the representation of objects and faces provided by inferior temporal cortex neurons in the primate (macaque) brain is followed by new findings about how this representation operates in natural scenes and allows a number of objects and their relative spatial position in a scene to be encoded. Then a computational approach to how the object recognition processes described are performed in the primate brain is discussed as well as the types of strategy that the human brain uses to solve the enormous computational problem of invariant object recognition in complex natural scenes (Rolls 2008b; Rolls and Deco 2002; Rolls and Stringer 2006b). This contribution aims to provide a closely linked neurophysiological and computational approach to object recognition and categorization. Other approaches are represented in this volume and elsewhere (Biederman 1987; Fukushima 1989; Riesenhuber and Poggio 2000; Serre et al. 2007), and are compared with current approaches (Rolls 2008b; Rolls and Deco 2002; Rolls and Stringer 2006b).
The Neurophysiology of Object Representation in the Inferior Temporal Visual Cortex
Some properties of the hierarchical organization of the primate ventral visual system that lead to the inferior temporal visual cortex (IT), where object representations are present (Rolls 2000; Rolls 2007b, 2008b; Rolls and Deco 2002), are shown in Figure 14.1. The receptive fields of neurons become larger related to the convergence from stage to stage, and the representation develops over the stages from features such as bars and edges, to combinations of features such as combinations of lines or colors in intermediate stages such as V4 (Hegde and Van Essen 2000; Ito and Komatsu 2004) to objects in IT.
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- Information
- Object CategorizationComputer and Human Vision Perspectives, pp. 257 - 287Publisher: Cambridge University PressPrint publication year: 2009
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