{"title":"Computer Perceptual Organization in Computer Vision","authors":"Sudeep Sarkar, K. Boyer","doi":"10.1142/2421","DOIUrl":null,"url":null,"abstract":"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 algorithm, is based on Bayesian probabilistic networks and has potential for being used in other spatial information tasks. The book also has a comprehensive review of the prior work in the area. It not only classifies the prior work but also identifies some areas of future work.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"84","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Series in Machine Perception and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/2421","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 84
Abstract
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 algorithm, is based on Bayesian probabilistic networks and has potential for being used in other spatial information tasks. The book also has a comprehensive review of the prior work in the area. It not only classifies the prior work but also identifies some areas of future work.