{"title":"用完整稳定的不变量描述子实现轮廓跟踪的形状距离","authors":"A. Mokadem, M. Daoudi, F. Ghorbel","doi":"10.1109/ICPR.1996.546000","DOIUrl":null,"url":null,"abstract":"We consider the problem of comparing geometric objects in order to determine the extent to which one object resembles another. Invariant feature families are presented. A complete and stable set of invariant features has been applied to define all invariant distance in the shapes space. This distance allows us to detect and follow moving objects in a dynamic scene. In order to evaluate the performance of such a metric, experimental results are given.","PeriodicalId":290297,"journal":{"name":"Proceedings of 13th International Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A shape distance by complete and stable invariant descriptors for contour tracking\",\"authors\":\"A. Mokadem, M. Daoudi, F. Ghorbel\",\"doi\":\"10.1109/ICPR.1996.546000\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the problem of comparing geometric objects in order to determine the extent to which one object resembles another. Invariant feature families are presented. A complete and stable set of invariant features has been applied to define all invariant distance in the shapes space. This distance allows us to detect and follow moving objects in a dynamic scene. In order to evaluate the performance of such a metric, experimental results are given.\",\"PeriodicalId\":290297,\"journal\":{\"name\":\"Proceedings of 13th International Conference on Pattern Recognition\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 13th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1996.546000\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 13th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1996.546000","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A shape distance by complete and stable invariant descriptors for contour tracking
We consider the problem of comparing geometric objects in order to determine the extent to which one object resembles another. Invariant feature families are presented. A complete and stable set of invariant features has been applied to define all invariant distance in the shapes space. This distance allows us to detect and follow moving objects in a dynamic scene. In order to evaluate the performance of such a metric, experimental results are given.
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