Texture classification based on EMD and FFT

被引：35

作者：

Xiong C.-Z. ^{[1
]}

Xu J.-Y. ^{[2
]}

Zou J.-C. ^{[3
]}

Qi D.-X. ^{[1
]}

机构：

[1] School of Information Science and Technology, Sun Yat-sen University

[2] Information Science School, Guangdong University of Business Studies

[3] College of Science, North China University of Technology

来源：

Journal of Zhejiang University-SCIENCE A | 2006年 / 7卷 / 9期

基金：

中国国家自然科学基金;

关键词：

Auto-registration; Empirical mode decomposition; Fourier transform; Rotation-invariant; Texture classification;

D O I：

10.1631/jzus.2006.A1516

中图分类号：

学科分类号：

摘要：

Empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process that reflects human's visual mechanism of differentiating textures. We present a modified 2D EMD algorithm using the FastRBF and an appropriate number of iterations in the shifting process (SP), then apply it to texture classification. Rotation-invariant texture feature vectors are extracted using auto-registration and circular regions of magnitude spectra of 2D fast Fourier transform (FFT). In the experiments, we employ a Bayesion classifier to classify a set of 15 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing datasets for images with different orientations, show the effectiveness of the proposed classification scheme.

引用

页码：1516 / 1521

页数：5

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