Empirical Mode Decomposition for Rotation Invariant Texture Classification

被引：0
|
作者：
Xiong Changzhen [1 ]
Guo Fenhong [2 ,3 ]
机构：
[1] North China Univ Technol, Lab Intelligent Transportat Syst, Beijing, Peoples R China
[2] North China Univ Technol, Coll Sci, Beijing, Peoples R China
[3] Sun Yat Sen Univ, Sch Informat Technol & Sci, Guangzhou, Guangdong, Peoples R China
来源：
2009 IEEE PACIFIC RIM CONFERENCE ON COMMUNICATIONS, COMPUTERS AND SIGNAL PROCESSING, VOLS 1 AND 2 | 2009年
关键词：
SEGMENTATION; FILTERS; ALGORITHM; FEATURES;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A novel and effective scheme for rotation invariant texture classification is presented using an adaptive and approximately orthogonal filtering process Bidimensional Empirical Mode Decomposition (BEMD). The extraction of rotation invariant feature for a given image involves BEMD and circular zones. A feature vector extracted from circular zones of intrinsic mode function (IMF) is constructed for rotation invariant texture classification. In the experiments, we use rotation invariant feature to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results show that the effectiveness of the proposed classification scheme compared with other classification methods.
引用
收藏
页码：551 / 554
页数：4
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