Image Segmentation Using Curve Evolution and Anisotropic Diffusion: An Integrated Approach

Yongsheng Pan, J. Birdwell, S. Djouadi
{"title":"Image Segmentation Using Curve Evolution and Anisotropic Diffusion: An Integrated Approach","authors":"Yongsheng Pan, J. Birdwell, S. Djouadi","doi":"10.1109/ISM.2005.68","DOIUrl":null,"url":null,"abstract":"In this paper, a new model is proposed for image segmentation that integrates the curve evolution and anisotropic diffusion methods. The curve evolution method, utilizing both gradient and region information, segments an image into multiple regions. During the evolution of the curve, anisotropic diffusion is adaptively applied to the image to remove noise while preserving boundary information. Coupled partial differential equations (PDE's) are used to implement the method. Experimental results show that the proposed model is successful for complex images with high noise","PeriodicalId":322363,"journal":{"name":"Seventh IEEE International Symposium on Multimedia (ISM'05)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seventh IEEE International Symposium on Multimedia (ISM'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISM.2005.68","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

In this paper, a new model is proposed for image segmentation that integrates the curve evolution and anisotropic diffusion methods. The curve evolution method, utilizing both gradient and region information, segments an image into multiple regions. During the evolution of the curve, anisotropic diffusion is adaptively applied to the image to remove noise while preserving boundary information. Coupled partial differential equations (PDE's) are used to implement the method. Experimental results show that the proposed model is successful for complex images with high noise
基于曲线演化和各向异性扩散的图像分割方法
本文提出了一种结合曲线演化和各向异性扩散方法的图像分割模型。曲线演化方法利用梯度和区域信息,将图像分割成多个区域。在曲线演化过程中,自适应地对图像应用各向异性扩散,在保持边界信息的同时去除噪声。采用耦合偏微分方程(PDE)来实现该方法。实验结果表明,该模型对于高噪声的复杂图像是有效的
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信