{"title":"曲面曲率估计的统计","authors":"A. Hilton, J. Illingworth, T. Windeatt","doi":"10.1109/ICPR.1994.576222","DOIUrl":null,"url":null,"abstract":"Reliable curvature estimation is an important goal in image analysis to provide viewpoint independent cues for shape classification. This paper presents a model of the relationship between the variance of curvature estimates and the image noise. Agreement to within 10% is obtained for 3D range data. Previous models have only provided qualitative agreement with experimental observations. A perturbation error analysis is performed on the local least square surface fitting algorithm which is commonly used to obtain partial derivative estimates in the presence of noise.","PeriodicalId":312019,"journal":{"name":"Proceedings of 12th International Conference on Pattern Recognition","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"Statistics of surface curvature estimates\",\"authors\":\"A. Hilton, J. Illingworth, T. Windeatt\",\"doi\":\"10.1109/ICPR.1994.576222\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reliable curvature estimation is an important goal in image analysis to provide viewpoint independent cues for shape classification. This paper presents a model of the relationship between the variance of curvature estimates and the image noise. Agreement to within 10% is obtained for 3D range data. Previous models have only provided qualitative agreement with experimental observations. A perturbation error analysis is performed on the local least square surface fitting algorithm which is commonly used to obtain partial derivative estimates in the presence of noise.\",\"PeriodicalId\":312019,\"journal\":{\"name\":\"Proceedings of 12th International Conference on Pattern Recognition\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 12th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1994.576222\",\"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 of 12th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1994.576222","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reliable curvature estimation is an important goal in image analysis to provide viewpoint independent cues for shape classification. This paper presents a model of the relationship between the variance of curvature estimates and the image noise. Agreement to within 10% is obtained for 3D range data. Previous models have only provided qualitative agreement with experimental observations. A perturbation error analysis is performed on the local least square surface fitting algorithm which is commonly used to obtain partial derivative estimates in the presence of noise.
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