第九章 中文题名(小二号字体加粗).doc
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中文题名(小号)
(中文题名应为名词性结构,简洁、明确、有吸引力,能突显出文章的主要内容和独特之处,尽量避免使用“探讨、初探、浅谈”等词语,一般不超过20个汉字
吕**1a,汪**2,李**1b,陈**1b(号黑体)
(1.浙江理工大学a.材料与纺织学院;b.先进纺织材料与制备技术教育部重点实验室,杭州 3100182.苏州大学 现代丝绸国家工程实验室,江苏 苏州 215123) (5号宋体)
TS195.644 文献标志码:A 文章编号:1001-7003(2011)0010-*** (中图分类号可在网站查询)
English title
Lü **1a, WANG**2, LI**1b, CHEN **1b
( 1a.College of Materials and Textiles; 1b.Key Laboratory of Advanced Textile Materials and Manufacturing Technology, Ministry of Education, Zhejiang Sci-Tech University, Hangzhou 310018, China; 2.National Engineering Laboratory for Modern Silk, Soochow University, Suzhou 215123, China )
Abstract: Texture is a key feature frequently used for image analysis and recognition, and wavelet transforms are common tools for image texture analysis and classification. However, wavelet-based texture classification methods usually neglect the information in the low-pass subband, and cannot capture piece-wise singularities contained in image texture. In this paper, we propose local energy histograms (LEHs) for modeling wavelet subband coefficients, Poisson mixture models (PMMs) for modeling contourlet subband features, and clustering for extracting contourlet subband features. Then, these modeling methods are utilized to texture classification. The LEH-based texture classification method alleviates the difficulty of modeling wavelet subband coefficients, the PMM-based texture classification method is the first to model contourlet subband features using Poisson mixture models, and the texture classification method based on clustering in contourelet subands is a fast classification approach. Experimental results reveal that our proposed methods outperform some current state-of-the-art texture classification methods.
Key words: directional multiscale transform; statistical modeling; Poisson mixture model; texture classification texture retrieval
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