{"title":"一种基于两阶段鲁棒统计的多类型特征时间配准方法","authors":"Gilles Simon, M. Berger","doi":"10.1109/ICCV.1998.710728","DOIUrl":null,"url":null,"abstract":"A model registration system capable of tracking an object, the model of which is known, in an image sequence is presented. It integrates tracking, pose determination and updating of the visible features. The heart of our system is the pose computation method, which handles various features (points, lines and free-form curves) in a very robust way and is able to give a correct estimate of the pose even when tracking errors occur. The reliability of the system is shown on an augmented reality project.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"59","resultStr":"{\"title\":\"A two-stage robust statistical method for temporal registration from features of various type\",\"authors\":\"Gilles Simon, M. Berger\",\"doi\":\"10.1109/ICCV.1998.710728\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A model registration system capable of tracking an object, the model of which is known, in an image sequence is presented. It integrates tracking, pose determination and updating of the visible features. The heart of our system is the pose computation method, which handles various features (points, lines and free-form curves) in a very robust way and is able to give a correct estimate of the pose even when tracking errors occur. The reliability of the system is shown on an augmented reality project.\",\"PeriodicalId\":270671,\"journal\":{\"name\":\"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)\",\"volume\":\"130 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-01-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"59\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCV.1998.710728\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCV.1998.710728","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A two-stage robust statistical method for temporal registration from features of various type
A model registration system capable of tracking an object, the model of which is known, in an image sequence is presented. It integrates tracking, pose determination and updating of the visible features. The heart of our system is the pose computation method, which handles various features (points, lines and free-form curves) in a very robust way and is able to give a correct estimate of the pose even when tracking errors occur. The reliability of the system is shown on an augmented reality project.