{"title":"纹理彩色图像的马尔可夫分割","authors":"M. Ameur, C. Daoui, N. Idrissi","doi":"10.1109/ISCV49265.2020.9204066","DOIUrl":null,"url":null,"abstract":"This paper presents an application of textured color images segmentation using hidden Markov chain model. We propose two comparative studies, The first one is between EM(Expectation-Maximization) algorithm,SEM (Stochastic Expectation-Maximization) algorithm and ICE (Iterative Conditional Estimation) algorithm, these estimators are used to estimate the parameters of Hidden Markov Chain with Independent Noise. The second one is between MAP(Maximum a Posteriori) and MPM(Maximum Marginal Posteriori) algorithms. These strategies are used to estimate the resulted images of segmentation. The obtained results show that EM algorithm, SEM, and ICE algorithm give the same results of segmentation under MAP and MPM algorithms. But, MPM provides better segmentation results than MAP.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Markovian Segmentation Of Textured Color Images\",\"authors\":\"M. Ameur, C. Daoui, N. Idrissi\",\"doi\":\"10.1109/ISCV49265.2020.9204066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an application of textured color images segmentation using hidden Markov chain model. We propose two comparative studies, The first one is between EM(Expectation-Maximization) algorithm,SEM (Stochastic Expectation-Maximization) algorithm and ICE (Iterative Conditional Estimation) algorithm, these estimators are used to estimate the parameters of Hidden Markov Chain with Independent Noise. The second one is between MAP(Maximum a Posteriori) and MPM(Maximum Marginal Posteriori) algorithms. These strategies are used to estimate the resulted images of segmentation. The obtained results show that EM algorithm, SEM, and ICE algorithm give the same results of segmentation under MAP and MPM algorithms. But, MPM provides better segmentation results than MAP.\",\"PeriodicalId\":313743,\"journal\":{\"name\":\"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCV49265.2020.9204066\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCV49265.2020.9204066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
介绍了隐马尔可夫链模型在纹理彩色图像分割中的应用。我们提出了两个比较研究,首先是EM(Expectation-Maximization)算法、SEM (Stochastic Expectation-Maximization)算法和ICE (Iterative Conditional Estimation)算法,这些估计器用于估计具有独立噪声的隐马尔可夫链的参数。第二个是MAP(Maximum a Posteriori)和MPM(Maximum Marginal Posteriori)算法之间的问题。这些策略用于估计分割后的结果图像。结果表明,EM算法、SEM算法和ICE算法在MAP和MPM算法下的分割结果相同。但MPM的分割效果优于MAP。
This paper presents an application of textured color images segmentation using hidden Markov chain model. We propose two comparative studies, The first one is between EM(Expectation-Maximization) algorithm,SEM (Stochastic Expectation-Maximization) algorithm and ICE (Iterative Conditional Estimation) algorithm, these estimators are used to estimate the parameters of Hidden Markov Chain with Independent Noise. The second one is between MAP(Maximum a Posteriori) and MPM(Maximum Marginal Posteriori) algorithms. These strategies are used to estimate the resulted images of segmentation. The obtained results show that EM algorithm, SEM, and ICE algorithm give the same results of segmentation under MAP and MPM algorithms. But, MPM provides better segmentation results than MAP.