{"title":"一种概率阴影不变颜色距离测度","authors":"S. Wesolkowski, P. Fieguth","doi":"10.5281/ZENODO.40594","DOIUrl":null,"url":null,"abstract":"We develop a probabilistic color distance measure based on hypothesis testing in order to achieve shading invariance in image segmentation. We derive this new color distance measure based on the Dichromatic Reflection Model and noise statistics. We show preliminary results of using the new semi-metric in a color image segmentation task to show its effectiveness.","PeriodicalId":176384,"journal":{"name":"2007 15th European Signal Processing Conference","volume":"276 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A probabilistic shading invariant color distance measure\",\"authors\":\"S. Wesolkowski, P. Fieguth\",\"doi\":\"10.5281/ZENODO.40594\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop a probabilistic color distance measure based on hypothesis testing in order to achieve shading invariance in image segmentation. We derive this new color distance measure based on the Dichromatic Reflection Model and noise statistics. We show preliminary results of using the new semi-metric in a color image segmentation task to show its effectiveness.\",\"PeriodicalId\":176384,\"journal\":{\"name\":\"2007 15th European Signal Processing Conference\",\"volume\":\"276 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 15th European Signal Processing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.40594\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 15th European Signal Processing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.40594","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A probabilistic shading invariant color distance measure
We develop a probabilistic color distance measure based on hypothesis testing in order to achieve shading invariance in image segmentation. We derive this new color distance measure based on the Dichromatic Reflection Model and noise statistics. We show preliminary results of using the new semi-metric in a color image segmentation task to show its effectiveness.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
