{"title":"捕获非刚性变形的分层非参数方法","authors":"A. Ecker, S. Ullman","doi":"10.1109/CRV.2005.6","DOIUrl":null,"url":null,"abstract":"We present a novel approach for measuring deformations between image patches. Our algorithm is a variant of dynamic programming that is not inherently one-dimensional, and its scores are on a relative scale. The method is based on the combination of similarities between many overlapping sub-patches. The algorithm is designed to be robust to small deformations of parts at various positions and scales.","PeriodicalId":307318,"journal":{"name":"The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A hierarchical nonparametric method for capturing nonrigid deformations\",\"authors\":\"A. Ecker, S. Ullman\",\"doi\":\"10.1109/CRV.2005.6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a novel approach for measuring deformations between image patches. Our algorithm is a variant of dynamic programming that is not inherently one-dimensional, and its scores are on a relative scale. The method is based on the combination of similarities between many overlapping sub-patches. The algorithm is designed to be robust to small deformations of parts at various positions and scales.\",\"PeriodicalId\":307318,\"journal\":{\"name\":\"The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CRV.2005.6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRV.2005.6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A hierarchical nonparametric method for capturing nonrigid deformations
We present a novel approach for measuring deformations between image patches. Our algorithm is a variant of dynamic programming that is not inherently one-dimensional, and its scores are on a relative scale. The method is based on the combination of similarities between many overlapping sub-patches. The algorithm is designed to be robust to small deformations of parts at various positions and scales.
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