{"title":"基于直方图的阈值分割算法分析","authors":"Glasbey C.A.","doi":"10.1006/cgip.1993.1040","DOIUrl":null,"url":null,"abstract":"<div><p>Eleven histogram-based global thresholding algorithms are presented in a common notational framework. Relationships among them are identified from 654 mixtures of two Gaussian distributions, plus effects of mixed pixels. The iterated version of Kittler and Illingworth′s minimum error algorithm (<em>Pattern Recognition</em>, 19, 1986, 41-47) is found to be best.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 6","pages":"Pages 532-537"},"PeriodicalIF":0.0000,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1040","citationCount":"653","resultStr":"{\"title\":\"An Analysis of Histogram-Based Thresholding Algorithms\",\"authors\":\"Glasbey C.A.\",\"doi\":\"10.1006/cgip.1993.1040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Eleven histogram-based global thresholding algorithms are presented in a common notational framework. Relationships among them are identified from 654 mixtures of two Gaussian distributions, plus effects of mixed pixels. The iterated version of Kittler and Illingworth′s minimum error algorithm (<em>Pattern Recognition</em>, 19, 1986, 41-47) is found to be best.</p></div>\",\"PeriodicalId\":100349,\"journal\":{\"name\":\"CVGIP: Graphical Models and Image Processing\",\"volume\":\"55 6\",\"pages\":\"Pages 532-537\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1006/cgip.1993.1040\",\"citationCount\":\"653\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CVGIP: Graphical Models and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1049965283710400\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CVGIP: Graphical Models and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1049965283710400","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Analysis of Histogram-Based Thresholding Algorithms
Eleven histogram-based global thresholding algorithms are presented in a common notational framework. Relationships among them are identified from 654 mixtures of two Gaussian distributions, plus effects of mixed pixels. The iterated version of Kittler and Illingworth′s minimum error algorithm (Pattern Recognition, 19, 1986, 41-47) is found to be best.
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