{"title":"基于模糊集的感知分组","authors":"Hang-Bong Kangt, E. Walker","doi":"10.1109/FUZZY.1992.258737","DOIUrl":null,"url":null,"abstract":"The authors propose a new approach based on fuzzy sets for detecting relations among points, line segments, and elliptic arc segments. Appropriate constraints defining the relations are extracted. A suitable fuzzy membership function is assigned to each constraint. Then the constraints are combined by fuzzy set operations to describe meaningful relations. According to these meaningful relations, a perceptual grouping is executed. Grouping methods are described on the basis of collinear and coelliptic relations. A measure of the significance of grouping for high-level vision processing is discussed. A prototype system for perceptual grouping from image data was implemented using a frame-based knowledge representation scheme, and experimental results for real image data are presented.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Perceptual grouping based on fuzzy sets\",\"authors\":\"Hang-Bong Kangt, E. Walker\",\"doi\":\"10.1109/FUZZY.1992.258737\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors propose a new approach based on fuzzy sets for detecting relations among points, line segments, and elliptic arc segments. Appropriate constraints defining the relations are extracted. A suitable fuzzy membership function is assigned to each constraint. Then the constraints are combined by fuzzy set operations to describe meaningful relations. According to these meaningful relations, a perceptual grouping is executed. Grouping methods are described on the basis of collinear and coelliptic relations. A measure of the significance of grouping for high-level vision processing is discussed. A prototype system for perceptual grouping from image data was implemented using a frame-based knowledge representation scheme, and experimental results for real image data are presented.<<ETX>>\",\"PeriodicalId\":222263,\"journal\":{\"name\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.1992.258737\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1992.258737","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The authors propose a new approach based on fuzzy sets for detecting relations among points, line segments, and elliptic arc segments. Appropriate constraints defining the relations are extracted. A suitable fuzzy membership function is assigned to each constraint. Then the constraints are combined by fuzzy set operations to describe meaningful relations. According to these meaningful relations, a perceptual grouping is executed. Grouping methods are described on the basis of collinear and coelliptic relations. A measure of the significance of grouping for high-level vision processing is discussed. A prototype system for perceptual grouping from image data was implemented using a frame-based knowledge representation scheme, and experimental results for real image data are presented.<>