摘要
介绍了一种利用局部沃尔什变换(LWT)提取图像纹理特征的新方法,给出LWT的定义,并分析了LWT系数的统计特性及其各阶矩的纹理鉴别性能。结果表明:自然纹理图像的LWT系数一般不服从正态分布,其偶数阶矩具有较好的纹理鉴别性能,奇数阶矩的纹理鉴别性能较差,因此选取LWT系数的偶数阶(2、4、6阶)矩作为纹理特征。与Haralick[1]、Wang和He[2,3],以及HuiYu[5]等人提出的纹理特征相比,基于LWT的纹理特征具有更好的鉴别性能,并且计算简单。
<Abstrcat>A new texture feature extraction method using Local Walsh Transform (LWT) is presented. The definition of LWT is given. The statistical properties of LWT coefficients are analyzed. The texture discrimination performance of the moments of LWT coefficients are investigated. Detail examinations reveal that the LWT coefficients of the natural texture images usually do not yield to Gauss distribution, their even-order moments have high texture discrimination performance, while their odd-order moments have low texture discrimination performance. Hence, the even-order (2^(nd), 4^(th), 6^(th) order) moments of the LWT coefficients are selected as texture features. Compared with the other texture features defined by Haralick^([1]),Wang and He^([2,3]), Hui Yu^([5]), the texture features we present have the best texture discrimination performance.
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2005年第3期86-91,共6页
Journal of National University of Defense Technology
基金
国家部委基金项目资助(41303040204)
关键词
沃尔什变换
纹理特征
纹理分析
图像处理
模式识别
Walsh transform
texture features
texture analysis
image processing
pattern recognition
