{"title":"图像分割的一致区域合并","authors":"F. Nielsen, R. Nock","doi":"10.1109/ACPR.2013.142","DOIUrl":null,"url":null,"abstract":"Image segmentation is a fundamental task of image processing that consists in partitioning the image by grouping pixels into homogeneous regions. We propose a novel segmentation algorithm that consists in combining many runs of a simple and fast randomized segmentation algorithm. Our algorithm also yields a soft-edge closed contour detector. We describe the theoretical probabilistic framework and report on our implementation that experimentally corroborates that performance increases with the number of runs.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Consensus Region Merging for Image Segmentation\",\"authors\":\"F. Nielsen, R. Nock\",\"doi\":\"10.1109/ACPR.2013.142\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image segmentation is a fundamental task of image processing that consists in partitioning the image by grouping pixels into homogeneous regions. We propose a novel segmentation algorithm that consists in combining many runs of a simple and fast randomized segmentation algorithm. Our algorithm also yields a soft-edge closed contour detector. We describe the theoretical probabilistic framework and report on our implementation that experimentally corroborates that performance increases with the number of runs.\",\"PeriodicalId\":365633,\"journal\":{\"name\":\"2013 2nd IAPR Asian Conference on Pattern Recognition\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 2nd IAPR Asian Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACPR.2013.142\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 2nd IAPR Asian Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACPR.2013.142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image segmentation is a fundamental task of image processing that consists in partitioning the image by grouping pixels into homogeneous regions. We propose a novel segmentation algorithm that consists in combining many runs of a simple and fast randomized segmentation algorithm. Our algorithm also yields a soft-edge closed contour detector. We describe the theoretical probabilistic framework and report on our implementation that experimentally corroborates that performance increases with the number of runs.