{"title":"基于运动估计的几何点集模式匹配","authors":"Zongqing Lu, Wang Zhao, Haiguang Guo, Cheng Jun","doi":"10.1109/CMSP.2011.168","DOIUrl":null,"url":null,"abstract":"We introduce a novel algorithm for solving the subpixel geometric point sets matching problem based on motion estimation. The crucial idea is to build the distance map images of discrete point sets, and use the optical flow scheme to obtain the final accurate offset. The original point sets matching problem is replaced by an image matching problem, which provides more data information and improve the matching stability.","PeriodicalId":309902,"journal":{"name":"2011 International Conference on Multimedia and Signal Processing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Geometric Point Sets Pattern Matching by Motion Estimation\",\"authors\":\"Zongqing Lu, Wang Zhao, Haiguang Guo, Cheng Jun\",\"doi\":\"10.1109/CMSP.2011.168\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce a novel algorithm for solving the subpixel geometric point sets matching problem based on motion estimation. The crucial idea is to build the distance map images of discrete point sets, and use the optical flow scheme to obtain the final accurate offset. The original point sets matching problem is replaced by an image matching problem, which provides more data information and improve the matching stability.\",\"PeriodicalId\":309902,\"journal\":{\"name\":\"2011 International Conference on Multimedia and Signal Processing\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Multimedia and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CMSP.2011.168\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Multimedia and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CMSP.2011.168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Geometric Point Sets Pattern Matching by Motion Estimation
We introduce a novel algorithm for solving the subpixel geometric point sets matching problem based on motion estimation. The crucial idea is to build the distance map images of discrete point sets, and use the optical flow scheme to obtain the final accurate offset. The original point sets matching problem is replaced by an image matching problem, which provides more data information and improve the matching stability.