Perceptual Organization for Artificial Vision Systems is an edited collection of invited contributions based on papers presented at The Workshop on Perceptual Organization in Computer Vision, held in Corfu, Greece, in September 1999. The theme of the workshop was `Assessing the State of the Community and Charting New Research Directions.' Perceptual organization can be defined as the ability to impose structural regularity on sensory data, so as to group sensory primitives arising from a common underlying cause. This book explores new models, theories, and algorithms for perceptual organization. Perceptual Organization for Artificial Vision Systems includes contributions by the world's leading researchers in the field. It explores new models, theories, and algorithms for perceptual organization, as well as demonstrates the means for bringing research results and theoretical principles to fruition in the construction of computer vision systems. The focus of this collection is on the design of artificial vision systems. The chapters comprise contributions from researchers in both computer vision and human vision.

This book presents an overview of different frameworks for understanding perceptual organization, and a state-of-the-art summary of the domain. It describes findings from visual search, illusory contours, and object recognition using electrophysiological measures.

Perceptual organization comprises a wide range of processes such as perceptual grouping, figure-ground organization, filling-in, completion, perceptual switching, etc. Such processes are most notable in the context of shape perception but they also play a role in texture perception, lightness perception, color perception, motion perception, depth perception, etc. Perceptual organization deals with a variety of perceptual phenomena of central interest, studied from many different perspectives, including psychophysics, experimental psychology, neuropsychology, neuroimaging, neurophysiology, and computational modeling. Given its central importance in phenomenal experience, perceptual organization has also figured prominently in classic Gestalt writings on the topic, touching upon deep philosophical issues regarding mind-brain relationships and consciousness. In addition, it attracts a great deal of interest from people working in applied areas like visual art, design, architecture, music, and so forth. The Oxford Handbook of Perceptual Organization provides a broad and extensive review of the current literature, written in an accessible form for scholars and students. With chapter written by leading researchers in the field, this is the state-of-the-art reference work on this topic, and will be so for many years to come.

This book describes the design of a complete, flexible system for perceptual organization in computer vision using graph theoretic techniques, voting methods, and an extension of the Bayesian networks called perceptual inference networks (PINs). The PIN, which forms the heart of the system and which is based on Bayesian probabilistic networks, exhibits potential for application in several areas of computer vision as well as a range of other spatial reasoning tasks. The text includes a highly comprehensive, classificatory review of prior work in perceptual organization and, within that framework, identifies key areas for future work by the computer vision research community.

An exploration of ideas emanating from behavioural, developmental, neurophysiological, neuropsychological and computational approaches to the problem of visual perceptual organization. It is based on papers presented at the 31st Carnegie Symposium on Cognition, held in June 2000.

This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. We show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The work presented here extends the original tensor voting framework with the addition of boundary inference capabilities; a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. We show complete analysis for some problems, while we briefly outline our approach for other applications and provide pointers to relevant sources.

"This book addresses the problem of how the human visual system organizes inputs that are fragmented in space and time into coherent, stable perceptual units - objects. In doing so it addresses the following questions: what kinds of segmentation and grouping abilities exist in human perceivers? What information and computational processes achieve segmentation and grouping? What are the psychological consequences of perceiving whole objects?" "From Fragments to Objects: Segmentation and Grouping in Vision takes a comprehensive cognitive science approach to object perception, brings together separate lines of research in object perception in one volume, gives an integrated and up-to-date review of theory and empirical research and offers directions for future study."--Jacket.