{"title":"基于约束最小二乘估计的随机图像分割模型","authors":"A. Kaup, T. Aach","doi":"10.1109/ISIT.1994.394630","DOIUrl":null,"url":null,"abstract":"The aim of the paper is to outline a layered statistical image model suitable for unsupervised image segmentation. The segment internal texture signal is described based on its spatial frequency representation while the image partition is modelled as a sample of a Gibbs/Markov random field. The most likely segmentation is estimated using a maximum a posteriori (MAP) formulation with the unknown parameters being determined by constrained least squares (CLS) estimation.<<ETX>>","PeriodicalId":331390,"journal":{"name":"Proceedings of 1994 IEEE International Symposium on Information Theory","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A stochastic model for image segmentation involving constrained least squares estimation\",\"authors\":\"A. Kaup, T. Aach\",\"doi\":\"10.1109/ISIT.1994.394630\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of the paper is to outline a layered statistical image model suitable for unsupervised image segmentation. The segment internal texture signal is described based on its spatial frequency representation while the image partition is modelled as a sample of a Gibbs/Markov random field. The most likely segmentation is estimated using a maximum a posteriori (MAP) formulation with the unknown parameters being determined by constrained least squares (CLS) estimation.<<ETX>>\",\"PeriodicalId\":331390,\"journal\":{\"name\":\"Proceedings of 1994 IEEE International Symposium on Information Theory\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE International Symposium on Information Theory\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIT.1994.394630\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE International Symposium on Information Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIT.1994.394630","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A stochastic model for image segmentation involving constrained least squares estimation
The aim of the paper is to outline a layered statistical image model suitable for unsupervised image segmentation. The segment internal texture signal is described based on its spatial frequency representation while the image partition is modelled as a sample of a Gibbs/Markov random field. The most likely segmentation is estimated using a maximum a posteriori (MAP) formulation with the unknown parameters being determined by constrained least squares (CLS) estimation.<>
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