Series in Machine Perception and Artificial Intelligence - Vol. 12
COMPUTER PERCEPTUAL ORGANIZATION IN COMPUTER VISION
by Sudeep Sarkar (Univ. South Florida) & Kim L Boyer (The Ohio State Univ.)
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.
Contents:
- Introduction
- A Brief History of Perceptual Organisation
Research
- Overview
- Preattentive Algorithm: Gestalt Graphs and Voting Methods
- Attentive Algroithm: The Perceptual Inference Network
- Structing a Perceptual Inference Network (PIN)
- Attentive Algorithm: Resource Management
- Evaluation
- Conclusion and Future Directions
Readership: Computer scientists and electrical engineers.
"The book makes good use of tables and figures to explain the algorithm and describe the issues in perceptual organization ... Overall, the book provides an introduction to the area of perceptual grouping in computer vision and gives a detailed description of one approach for gouping at many levels."
Keith Price
Computing Reviews, 1996 |
| 252pp |
Pub. date: Jul 1994 |
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