{"title":"聚类分类特征的关键主图像直方图分量","authors":"B. Homnan, W. Benjapolakul","doi":"10.1109/IEECON.2014.6925863","DOIUrl":null,"url":null,"abstract":"There are a lot of limit image histogram components in any image. This paper selects the principal image histogram components and evaluates them to get the critical one. As the central value of the image the critical principal image histogram component hold clusters and their distributions in the image. On the concept of the pixel distance, determinate mathematical model of probability and cumulative density functions categorize image subclusters and their member details with the threshold of the difference and the threshold of the number of pixels, within the image coverage of the critical principal image histogram component.","PeriodicalId":306512,"journal":{"name":"2014 International Electrical Engineering Congress (iEECON)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Cluster classification characteristics of the critical principal image histogram component\",\"authors\":\"B. Homnan, W. Benjapolakul\",\"doi\":\"10.1109/IEECON.2014.6925863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There are a lot of limit image histogram components in any image. This paper selects the principal image histogram components and evaluates them to get the critical one. As the central value of the image the critical principal image histogram component hold clusters and their distributions in the image. On the concept of the pixel distance, determinate mathematical model of probability and cumulative density functions categorize image subclusters and their member details with the threshold of the difference and the threshold of the number of pixels, within the image coverage of the critical principal image histogram component.\",\"PeriodicalId\":306512,\"journal\":{\"name\":\"2014 International Electrical Engineering Congress (iEECON)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Electrical Engineering Congress (iEECON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEECON.2014.6925863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Electrical Engineering Congress (iEECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEECON.2014.6925863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cluster classification characteristics of the critical principal image histogram component
There are a lot of limit image histogram components in any image. This paper selects the principal image histogram components and evaluates them to get the critical one. As the central value of the image the critical principal image histogram component hold clusters and their distributions in the image. On the concept of the pixel distance, determinate mathematical model of probability and cumulative density functions categorize image subclusters and their member details with the threshold of the difference and the threshold of the number of pixels, within the image coverage of the critical principal image histogram component.