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
20 - The Proactive Brain: Using Memory-Based Predictions in Visual Recognition
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
You are speeding along an empty stretch of highway in the southwestern Nevada desert on a clear, starlit night. It is very late, and you have been driving for many hours, fighting boredom and encroaching sleepiness. Suddenly you notice a strange, dark shape near the road, a few hundred yards away. What is this thing? Your mind is racing through the possibilities: Is it a police cruiser parked at an odd angle? An unusual rock formation? Some top-secret weapons system that the U.S. military is testing in the desert? As whatever-that-thing-is fades in the rearview mirror, you still have no idea what it was. Perhaps it was just a harmless piece of highway maintenance equipment that became unrecognizable at night?
The hypothetical example just described demonstrates a failure of the visual system to map input to any stored memory of an object. It is remarkable because of its rarity – most of our conscious visual recognition experience consists of recognizing things in our environment seemingly without effort. Recognition, by definition, relies on matching input to representations already stored in the brain. However, given that any real visual stimulus can be seen literally in an infinite number of views, determined by its orientation, lighting, distance, visual “noise,” and occlusion, it is highly improbable that the brain performs recognition by matching detailed input to a representation of every possible viewof an object.
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- Information
- Object CategorizationComputer and Human Vision Perspectives, pp. 384 - 400Publisher: Cambridge University PressPrint publication year: 2009
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