{"title":"协同光滑表面立体","authors":"T. Boult, Liang-Hua Chen","doi":"10.1109/CCV.1988.589980","DOIUrl":null,"url":null,"abstract":"This paper presents a new algorithm for stereo matching. The algo- rithm combines what are generally three processes, feature matching, surface reconstruction, and segmentation of world surfaces, in a consis- tent and synergistic way. By integrating these phases, which are usually sequential, the algorithm can make use of the current surface approxi- mation to disambiguate potential matches. This results in higher data densities, a consistency of interpretation, and greater system flexibility. Examples of the algorithm are presented on real and synthetic images, including a scene with a transparent surface.","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"159 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Synergistic Smooth Surface Stereo\",\"authors\":\"T. Boult, Liang-Hua Chen\",\"doi\":\"10.1109/CCV.1988.589980\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new algorithm for stereo matching. The algo- rithm combines what are generally three processes, feature matching, surface reconstruction, and segmentation of world surfaces, in a consis- tent and synergistic way. By integrating these phases, which are usually sequential, the algorithm can make use of the current surface approxi- mation to disambiguate potential matches. This results in higher data densities, a consistency of interpretation, and greater system flexibility. Examples of the algorithm are presented on real and synthetic images, including a scene with a transparent surface.\",\"PeriodicalId\":229545,\"journal\":{\"name\":\"[1988 Proceedings] Second International Conference on Computer Vision\",\"volume\":\"159 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1988-12-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1988 Proceedings] Second International Conference on Computer Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCV.1988.589980\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1988 Proceedings] Second International Conference on Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCV.1988.589980","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a new algorithm for stereo matching. The algo- rithm combines what are generally three processes, feature matching, surface reconstruction, and segmentation of world surfaces, in a consis- tent and synergistic way. By integrating these phases, which are usually sequential, the algorithm can make use of the current surface approxi- mation to disambiguate potential matches. This results in higher data densities, a consistency of interpretation, and greater system flexibility. Examples of the algorithm are presented on real and synthetic images, including a scene with a transparent surface.