{"title":"一种三维图像分割算法","authors":"Ding Zhi, Dong Yu-ning","doi":"10.1109/ICIG.2007.39","DOIUrl":null,"url":null,"abstract":"We propose a new 3D image segmentation method based on prediction, block-matching and partial 3D constraint in this paper. The algorithm only needs to set a few key points in the first image. We use intensity information and block-matching to optimize the initial condition, and consider the 3D object's characteristics at the same time. By using the partial 3D constraints we can get the result of 2D and 3D smoothness. Experimental results validate its usefulness in 3D image segmentation.","PeriodicalId":367106,"journal":{"name":"Fourth International Conference on Image and Graphics (ICIG 2007)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"An Algorithm for 3D Image Segmentation\",\"authors\":\"Ding Zhi, Dong Yu-ning\",\"doi\":\"10.1109/ICIG.2007.39\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a new 3D image segmentation method based on prediction, block-matching and partial 3D constraint in this paper. The algorithm only needs to set a few key points in the first image. We use intensity information and block-matching to optimize the initial condition, and consider the 3D object's characteristics at the same time. By using the partial 3D constraints we can get the result of 2D and 3D smoothness. Experimental results validate its usefulness in 3D image segmentation.\",\"PeriodicalId\":367106,\"journal\":{\"name\":\"Fourth International Conference on Image and Graphics (ICIG 2007)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fourth International Conference on Image and Graphics (ICIG 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIG.2007.39\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fourth International Conference on Image and Graphics (ICIG 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIG.2007.39","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We propose a new 3D image segmentation method based on prediction, block-matching and partial 3D constraint in this paper. The algorithm only needs to set a few key points in the first image. We use intensity information and block-matching to optimize the initial condition, and consider the 3D object's characteristics at the same time. By using the partial 3D constraints we can get the result of 2D and 3D smoothness. Experimental results validate its usefulness in 3D image segmentation.
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