{"title":"各种图像分割方法的分析与性能评价","authors":"S. U. Mageswari, C. Mala","doi":"10.1109/IC3I.2014.7019614","DOIUrl":null,"url":null,"abstract":"Image segmentation is a primary stage in image processing for identifying objects of interest. Segmentation methods are classified into region based, transform based, edge based and clustering based segmentation. In this paper, segmentation methods including histogram, watershed, Canny edge detector and K-means clustering techniques are studied and analyzed. The experimental results obtained are compared with different evaluation measures including three standard image segmentation indices: rand index, globally consistency error and variation of information.","PeriodicalId":430848,"journal":{"name":"2014 International Conference on Contemporary Computing and Informatics (IC3I)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Analysis and performance evaluation of various image segmentation methods\",\"authors\":\"S. U. Mageswari, C. Mala\",\"doi\":\"10.1109/IC3I.2014.7019614\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image segmentation is a primary stage in image processing for identifying objects of interest. Segmentation methods are classified into region based, transform based, edge based and clustering based segmentation. In this paper, segmentation methods including histogram, watershed, Canny edge detector and K-means clustering techniques are studied and analyzed. The experimental results obtained are compared with different evaluation measures including three standard image segmentation indices: rand index, globally consistency error and variation of information.\",\"PeriodicalId\":430848,\"journal\":{\"name\":\"2014 International Conference on Contemporary Computing and Informatics (IC3I)\",\"volume\":\"83 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Contemporary Computing and Informatics (IC3I)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC3I.2014.7019614\",\"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 Conference on Contemporary Computing and Informatics (IC3I)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC3I.2014.7019614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis and performance evaluation of various image segmentation methods
Image segmentation is a primary stage in image processing for identifying objects of interest. Segmentation methods are classified into region based, transform based, edge based and clustering based segmentation. In this paper, segmentation methods including histogram, watershed, Canny edge detector and K-means clustering techniques are studied and analyzed. The experimental results obtained are compared with different evaluation measures including three standard image segmentation indices: rand index, globally consistency error and variation of information.
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