纹理图像的鲁棒分形分析

N. Avadhanam, S. Mitra
{"title":"纹理图像的鲁棒分形分析","authors":"N. Avadhanam, S. Mitra","doi":"10.1109/IAI.1994.336692","DOIUrl":null,"url":null,"abstract":"A maximum likelihood estimation (MLE) method is used to estimate the fractal dimension of a number of natural texture images with and without the presence of noise. An additional texture measure which can be linked to the lacunarity measure is used to characterize natural textures since fractal dimension alone cannot totally characterize texture images. Segmentation of natural textures is successfully achieved by a k-means clustering algorithm using fractal dimension and the additional measure as representative features.<<ETX>>","PeriodicalId":438137,"journal":{"name":"Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Analysis of texture images using robust fractal description\",\"authors\":\"N. Avadhanam, S. Mitra\",\"doi\":\"10.1109/IAI.1994.336692\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A maximum likelihood estimation (MLE) method is used to estimate the fractal dimension of a number of natural texture images with and without the presence of noise. An additional texture measure which can be linked to the lacunarity measure is used to characterize natural textures since fractal dimension alone cannot totally characterize texture images. Segmentation of natural textures is successfully achieved by a k-means clustering algorithm using fractal dimension and the additional measure as representative features.<<ETX>>\",\"PeriodicalId\":438137,\"journal\":{\"name\":\"Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAI.1994.336692\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAI.1994.336692","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

利用极大似然估计(MLE)方法,对有噪声和无噪声的自然纹理图像进行分形维数估计。由于分形维数本身不能完全表征纹理图像,因此可以使用与空隙度度量相关联的附加纹理度量来表征自然纹理。采用分形维数和附加测度为代表特征的k-means聚类算法,成功实现了自然纹理的分割。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Analysis of texture images using robust fractal description
A maximum likelihood estimation (MLE) method is used to estimate the fractal dimension of a number of natural texture images with and without the presence of noise. An additional texture measure which can be linked to the lacunarity measure is used to characterize natural textures since fractal dimension alone cannot totally characterize texture images. Segmentation of natural textures is successfully achieved by a k-means clustering algorithm using fractal dimension and the additional measure as representative features.<>
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
自引率
0.00%
发文量
0
期刊最新文献
Frequency domain measurement of meteorological range from aircraft images Radar imaging using 2D adaptive non-parametric extrapolation and autoregressive modeling An algorithm for 3D scene description in an unknown environment New morphological operators: BERD and BDRE Theoretical and experimental comparison of the Lorenz information measure, entropy, and the mean absolute error
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1