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.

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.

COMPUTER VISION is a field of research that encompasses many objectives. A primary goal has been to construct visual sensors that can provide general-purpose robots with the same information about their surroundings as we receive from our own visual senses. This book takes an important step towards this goal by describing a working computer vision system named SCERPO. This system can recognize known three-dimensional objects in ordinary black-and-white images taken from unknown viewpoints, even when parts of the object are undetectable or hidden from view. A second major goal of computer vision re search is to provide a computational understanding of human vision. The research presented in this book has many implica tions for our understanding of human vision, particularly in the areas of perceptual organization and knowledge-based recogni tion. An attempt has been made to relate each computational result to the relevant areas in the psychology of vision. Since the material is meant to be accessible to a wide range of inter disciplinary readers, the book is written in plain language and attempts to explain most concepts from the starting position of the non-specialist. vii viii PREFACE One of the most important conclusions ansmg from this research is that visual recognition can commonly be achieved directly from the two-dimensional image without any prelim inary reconstruction of depth information or surface orienta tion from the visual input.