{"title":"基于颜色的图像分割","authors":"A. Roy, S. Parui, Amitav Paul, Utpal Roy","doi":"10.1109/ICIT.2008.29","DOIUrl":null,"url":null,"abstract":"This article addresses color image segmentation in hue-saturation space. A model for circular data is provided by the vM-Gauss distribution, which is a joint distribution of von-Mises and Gaussian distribution. The mixture of vM-Gauss distribution is used to model hue-saturation data. A cluster merging process is applied to separate such identifiable objects in the image. The results are shown on Berkeley segmentation dataset. A cluster association methodology is developed for comparison.","PeriodicalId":184201,"journal":{"name":"2008 International Conference on Information Technology","volume":"173 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Color Based Image Segmentation\",\"authors\":\"A. Roy, S. Parui, Amitav Paul, Utpal Roy\",\"doi\":\"10.1109/ICIT.2008.29\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article addresses color image segmentation in hue-saturation space. A model for circular data is provided by the vM-Gauss distribution, which is a joint distribution of von-Mises and Gaussian distribution. The mixture of vM-Gauss distribution is used to model hue-saturation data. A cluster merging process is applied to separate such identifiable objects in the image. The results are shown on Berkeley segmentation dataset. A cluster association methodology is developed for comparison.\",\"PeriodicalId\":184201,\"journal\":{\"name\":\"2008 International Conference on Information Technology\",\"volume\":\"173 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIT.2008.29\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2008.29","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This article addresses color image segmentation in hue-saturation space. A model for circular data is provided by the vM-Gauss distribution, which is a joint distribution of von-Mises and Gaussian distribution. The mixture of vM-Gauss distribution is used to model hue-saturation data. A cluster merging process is applied to separate such identifiable objects in the image. The results are shown on Berkeley segmentation dataset. A cluster association methodology is developed for comparison.
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