- Title Pages
- Preface
- Contributors
- Workshop Attendees
- SECTION I Introduction to Section I: Theory and Fundamentals
- CHAPTER 1 Ideal-Observer Models of Cue Integration
- CHAPTER 2 Causal Inference in Sensorimotor Learning and Control
- CHAPTER 3 The Role of Generative Knowledge in Object Perception
- CHAPTER 4 Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
- CHAPTER 5 Modeling Cue Integration in Cluttered Environments
- CHAPTER 6 Recruitment of New Visual Cues for Perceptual Appearance
- CHAPTER 7 Combining Image Signals before Three-Dimensional Reconstruction: The Intrinsic Constraint Model of Cue Integration
- CHAPTER 8 Cue Combination: Beyond Optimality
- SECTION II Introduction to Section II: Behavioral Studies
- CHAPTER 9 Priors and Learning in Cue Integration
- CHAPTER 10 Multisensory Integration and Calibration in Adults and in Children
- CHAPTER 11 The Statistical Relationship between Depth, Visual Cues, and Human Perception
- CHAPTER 12 Multisensory Perception: From Integration to Remapping
- CHAPTER 13 Humans' Multisensory Perception, from Integration to Segregation, Follows Bayesian Inference
- CHAPTER 14 Cues and Pseudocues in Texture and Shape Perception
- CHAPTER 15 Optimality Principles Apply to a Broad Range of Information Integration Problems in Perception and Action
- Section III Introduction to Section III: Neural Implementation
- CHAPTER 16 Self-Motion Perception: Multisensory Integration in Extrastriate Visual Cortex
- CHAPTER 17 Probing Neural Correlates of Cue Integration
- CHAPTER 18 Computational Models of Multisensory Integration in the Cat Superior Colliculus
- CHAPTER 19 Decoding the Cortical Representation of Depth
- CHAPTER 20 Dynamic Cue Combination in Distributional Population Code Networks
- CHAPTER 21 A Neural Implementation of Optimal Cue Integration
- CHAPTER 22 Contextual Modulations of Visual Receptive Fields: A Bayesian Perspective
- Index

# Modeling Cue Integration in Cluttered Environments

# Modeling Cue Integration in Cluttered Environments

- Chapter:
- (p.82) CHAPTER 5 Modeling Cue Integration in Cluttered Environments
- Source:
- Sensory Cue Integration
- Author(s):
### Maneesh Sahani

### Louise Whiteley

- Publisher:
- Oxford University Press

This chapter lays out one approach to describing the inferential problem encountered when integrating multiple different cues that may arise from many different objects. By switching representations from a set of discrete single-valued cues to a spatial representation based on attribute and cue “maps,” it was possible naturally to model observers' behavior in some simple multiobject and multicue settings, and provide a natural, tractable approach to approximation within these settings. But while effective in these simple cases, the framework is still far from providing a complete description of perceptual inference and integration in cluttered scenes. The framework developed here works best when the cues used for inference are inherently localized in space (in the visual case) or with respect to some other dimension important for determining grouping.

*Keywords:*
cue integration, single-valued cues, spatial representation, attribute maps, cue maps, perceptual inference, modeling

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- Title Pages
- Preface
- Contributors
- Workshop Attendees
- SECTION I Introduction to Section I: Theory and Fundamentals
- CHAPTER 1 Ideal-Observer Models of Cue Integration
- CHAPTER 2 Causal Inference in Sensorimotor Learning and Control
- CHAPTER 3 The Role of Generative Knowledge in Object Perception
- CHAPTER 4 Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
- CHAPTER 5 Modeling Cue Integration in Cluttered Environments
- CHAPTER 6 Recruitment of New Visual Cues for Perceptual Appearance
- CHAPTER 7 Combining Image Signals before Three-Dimensional Reconstruction: The Intrinsic Constraint Model of Cue Integration
- CHAPTER 8 Cue Combination: Beyond Optimality
- SECTION II Introduction to Section II: Behavioral Studies
- CHAPTER 9 Priors and Learning in Cue Integration
- CHAPTER 10 Multisensory Integration and Calibration in Adults and in Children
- CHAPTER 11 The Statistical Relationship between Depth, Visual Cues, and Human Perception
- CHAPTER 12 Multisensory Perception: From Integration to Remapping
- CHAPTER 13 Humans' Multisensory Perception, from Integration to Segregation, Follows Bayesian Inference
- CHAPTER 14 Cues and Pseudocues in Texture and Shape Perception
- CHAPTER 15 Optimality Principles Apply to a Broad Range of Information Integration Problems in Perception and Action
- Section III Introduction to Section III: Neural Implementation
- CHAPTER 16 Self-Motion Perception: Multisensory Integration in Extrastriate Visual Cortex
- CHAPTER 17 Probing Neural Correlates of Cue Integration
- CHAPTER 18 Computational Models of Multisensory Integration in the Cat Superior Colliculus
- CHAPTER 19 Decoding the Cortical Representation of Depth
- CHAPTER 20 Dynamic Cue Combination in Distributional Population Code Networks
- CHAPTER 21 A Neural Implementation of Optimal Cue Integration
- CHAPTER 22 Contextual Modulations of Visual Receptive Fields: A Bayesian Perspective
- Index