一种基于自动阈值选择的图像分割新方法

2010 Sixth International Conference on Semantics, Knowledge and Grids Pub Date : 2010-11-01 DOI:10.1109/SKG.2010.72

Y. Zou, Bencheng Chai, Qili Xiao

{"title":"一种基于自动阈值选择的图像分割新方法","authors":"Y. Zou, Bencheng Chai, Qili Xiao","doi":"10.1109/SKG.2010.72","DOIUrl":null,"url":null,"abstract":"This paper presents a new image segmentation approach by suing automatic threshold selection. This approach divides the image into 2 class. The best threshold is got when the separation between the interclass variance and the variance between clasters is the maximal. Experimental results show this new approach can segment images automatically and quickly.","PeriodicalId":105513,"journal":{"name":"2010 Sixth International Conference on Semantics, Knowledge and Grids","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Image Segmentation Approach by Using Automation Threshold Selection\",\"authors\":\"Y. Zou, Bencheng Chai, Qili Xiao\",\"doi\":\"10.1109/SKG.2010.72\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new image segmentation approach by suing automatic threshold selection. This approach divides the image into 2 class. The best threshold is got when the separation between the interclass variance and the variance between clasters is the maximal. Experimental results show this new approach can segment images automatically and quickly.\",\"PeriodicalId\":105513,\"journal\":{\"name\":\"2010 Sixth International Conference on Semantics, Knowledge and Grids\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Sixth International Conference on Semantics, Knowledge and Grids\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SKG.2010.72\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Sixth International Conference on Semantics, Knowledge and Grids","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SKG.2010.72","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

本文提出了一种基于自动阈值选择的图像分割方法。该方法将图像分为2类。当类间方差和类间方差的分离最大时，得到最佳阈值。实验结果表明，该方法能够自动、快速地分割图像。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A New Image Segmentation Approach by Using Automation Threshold Selection

This paper presents a new image segmentation approach by suing automatic threshold selection. This approach divides the image into 2 class. The best threshold is got when the separation between the interclass variance and the variance between clasters is the maximal. Experimental results show this new approach can segment images automatically and quickly.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2010 Sixth International Conference on Semantics, Knowledge and Grids

自引率

0.00%

发文量