{"title":"基于多尺度信念传播的图像分割","authors":"Shifeng Chen, Yu Qiao","doi":"10.1109/ICINFA.2011.5949123","DOIUrl":null,"url":null,"abstract":"Image segmentation plays an important role in computer vision and image analysis. In this paper, we develop a novel algorithm which can automatically segment an image into regions with relative uniform texture or color without the need to decide the region number in advance. In this work, the segmentation is formulated as a labeling problem in the Markov random fields (MRFs) model. An efficient multi-scale belief propagation (BP) algorithm is used to find the solution to the MRF estimation. Extensive experiments have shown the effectiveness of our approach.","PeriodicalId":299418,"journal":{"name":"2011 IEEE International Conference on Information and Automation","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image segmentation via multi-scaled belief propagation\",\"authors\":\"Shifeng Chen, Yu Qiao\",\"doi\":\"10.1109/ICINFA.2011.5949123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image segmentation plays an important role in computer vision and image analysis. In this paper, we develop a novel algorithm which can automatically segment an image into regions with relative uniform texture or color without the need to decide the region number in advance. In this work, the segmentation is formulated as a labeling problem in the Markov random fields (MRFs) model. An efficient multi-scale belief propagation (BP) algorithm is used to find the solution to the MRF estimation. Extensive experiments have shown the effectiveness of our approach.\",\"PeriodicalId\":299418,\"journal\":{\"name\":\"2011 IEEE International Conference on Information and Automation\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Conference on Information and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINFA.2011.5949123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Information and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2011.5949123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image segmentation via multi-scaled belief propagation
Image segmentation plays an important role in computer vision and image analysis. In this paper, we develop a novel algorithm which can automatically segment an image into regions with relative uniform texture or color without the need to decide the region number in advance. In this work, the segmentation is formulated as a labeling problem in the Markov random fields (MRFs) model. An efficient multi-scale belief propagation (BP) algorithm is used to find the solution to the MRF estimation. Extensive experiments have shown the effectiveness of our approach.