{"title":"与毫米雷达图像匹配的制图","authors":"S. Moss, E. Hancock","doi":"10.1109/ACV.1996.572003","DOIUrl":null,"url":null,"abstract":"This paper describes an application of the EM (expectation and maximisation) algorithm to the registration of incomplete millimetric radar images. The data used in this study consists of a series of nonoverlapping radar sweeps. Our registration process aims to recover transformation parameters between the radar-data and a digital map. The tokens used in the matching process are fragmented line-segments extracted from the radar images which predominantly correspond to hedge-rows in the cartographic data. The EM technique models data uncertainty using Gaussian mixtures defined over the positions and orientations of the lines. The resulting weighted least-squares parameter estimation problem is solved using the Levenberg-Marquardt method. A sensitivity analysis reveals that the date-likelihood function is unimodal in the translation and scale parameters. In-fact the algorithm is only sensitive to the choice of initial rotation parameter; this is attributable to local suboptima in the log-likelihood function associated with /spl pi//3 orientation ambiguities in the map. The method is also demonstrated to be relatively insensitive to random measurement errors on the line-segments.","PeriodicalId":222106,"journal":{"name":"Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Cartographic matching with millimetre radar images\",\"authors\":\"S. Moss, E. Hancock\",\"doi\":\"10.1109/ACV.1996.572003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes an application of the EM (expectation and maximisation) algorithm to the registration of incomplete millimetric radar images. The data used in this study consists of a series of nonoverlapping radar sweeps. Our registration process aims to recover transformation parameters between the radar-data and a digital map. The tokens used in the matching process are fragmented line-segments extracted from the radar images which predominantly correspond to hedge-rows in the cartographic data. The EM technique models data uncertainty using Gaussian mixtures defined over the positions and orientations of the lines. The resulting weighted least-squares parameter estimation problem is solved using the Levenberg-Marquardt method. A sensitivity analysis reveals that the date-likelihood function is unimodal in the translation and scale parameters. In-fact the algorithm is only sensitive to the choice of initial rotation parameter; this is attributable to local suboptima in the log-likelihood function associated with /spl pi//3 orientation ambiguities in the map. The method is also demonstrated to be relatively insensitive to random measurement errors on the line-segments.\",\"PeriodicalId\":222106,\"journal\":{\"name\":\"Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACV.1996.572003\",\"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 Third IEEE Workshop on Applications of Computer Vision. WACV'96","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACV.1996.572003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cartographic matching with millimetre radar images
This paper describes an application of the EM (expectation and maximisation) algorithm to the registration of incomplete millimetric radar images. The data used in this study consists of a series of nonoverlapping radar sweeps. Our registration process aims to recover transformation parameters between the radar-data and a digital map. The tokens used in the matching process are fragmented line-segments extracted from the radar images which predominantly correspond to hedge-rows in the cartographic data. The EM technique models data uncertainty using Gaussian mixtures defined over the positions and orientations of the lines. The resulting weighted least-squares parameter estimation problem is solved using the Levenberg-Marquardt method. A sensitivity analysis reveals that the date-likelihood function is unimodal in the translation and scale parameters. In-fact the algorithm is only sensitive to the choice of initial rotation parameter; this is attributable to local suboptima in the log-likelihood function associated with /spl pi//3 orientation ambiguities in the map. The method is also demonstrated to be relatively insensitive to random measurement errors on the line-segments.