局部Walsh谱描述图像纹理特征的旋转不变性

孙慧贤; 张玉华; 罗飞路

doi:10.3788/OPE.20101808.1886

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局部Walsh谱描述图像纹理特征的旋转不变性

信息科学 | 更新时间：2020-08-12

- 局部Walsh谱描述图像纹理特征的旋转不变性
- Description of rotation invariant texture image with local Walsh spectrum
- 光学精密工程 2010年18卷第8期页码：1886-1895
- 作者机构：
  
  国防科技大学机电工程与自动化学院,湖南长沙,410072
- 作者简介：
- 基金信息：
  
  国防预研重点资助项目(No.51317030106)()
- DOI：10.3788/OPE.20101808.1886
  中图分类号： TP391.4
- 收稿日期：2009-11-26，
  
  修回日期：2009-12-28，
  
  网络出版日期：2010-08-20，
  
  纸质出版日期：2010-08-20
- 稿件说明：
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孙慧贤, 张玉华, 罗飞路. 局部Walsh谱描述图像纹理特征的旋转不变性[J]. 光学精密工程, 2010,18(8): 1886-1895

SUN Hui-xian, ZHANG Yu-hua, LUO Fei-lu. Description of rotation invariant texture image with local Walsh spectrum[J]. 光学精密工程, 2010,18(8): 1886-1895
孙慧贤, 张玉华, 罗飞路. 局部Walsh谱描述图像纹理特征的旋转不变性[J]. 光学精密工程, 2010,18(8): 1886-1895 DOI： 10.3788/OPE.20101808.1886.

SUN Hui-xian, ZHANG Yu-hua, LUO Fei-lu. Description of rotation invariant texture image with local Walsh spectrum[J]. 光学精密工程, 2010,18(8): 1886-1895 DOI： 10.3788/OPE.20101808.1886.

摘要

为了有效地进行纹理分析

提出一种基于局部Walsh谱的纹理特征旋转不变性描述方法。首先

比较每个像素点与其邻近点的灰度值生成局部二值序列

并计算序列离散Walsh变换的功率谱;然后

采用功率谱的各谱点值构成特征直方图描述纹理特征;最后

从序列的列率特性出发

构造了新的两族局部Walsh谱

揭示了局部Walsh谱与局部二值模式之间的联系。因为离散Walsh变换功率谱具有循环移位不变性

所以局部Walsh谱具有先天的旋转不变性。实验结果显示

与灰度共生矩阵和Gabor滤波器组相比

局部Walsh谱的纹理分类准确率较高;与局部二值模式相比

在相同尺度下局部Walsh谱的分类准确率比其高出3%以上

对两幅旋转纹理图像分割的错误率比其低11%和3%

表明提出的方法具有较好的纹理鉴别能力和旋转不变性。

Abstract

In order to analyze the image texture effectively

the new rotation invariant and multiresolution texture descriptors are proposed based on Local Walsh Spectrum (LWS). Firstly

the Local Binary Sequence (LBS) of each pixel is obtained by comparing its gray-scale with neighboring points and the power spectrum of Discrete Walsh Transform (DWT) of the LBS is calculated. Then

the spectrums value in the power spectrum is expressed in characteristic histogram to describe the texture feature. Finally

based on the sequency characteristic of LBS

the Two-family Sequency LWS (TSLWS) is proposed to reveal the relationship between LWS and Local Binary Pattern (LBP).Because of the circular-shift-invariant of DWT power spectrum

the proposed texture descriptors show prior rotation invariance. Experimental results indicate that the texture classification precisions of LWS are better than those of the Gray Level Co-occurrence Matrix (GLCM) method and Gabor filter bank method. Furthermore

as compared with the LBP

the texture classification precision of the LWS is 3% higher than that of LBP in the same local neighborhood and the segmentation in inaccuracies of the LWS are 11% and 23% respectively less than those of LBP for two rotated mosaic texture images

which proves that the proposed method has better abilities of texture discrimination and rotation invariance.

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Keywords

references

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