V-系统与Radon变换相结合的纹理分类算法  

Novel Algorithm for Image Texture Classification Combined the V-system with Radon Transform

作　　者：宋瑞霞[1] 王俊[1] 王小春[2] 郭芬红[1] 徐燕青[1] 齐东旭[1] 

机构地区：[1]北方工业大学理学院,北京100144 [2]北京林业大学理学院,北京100083

出　　处：《计算机辅助设计与图形学学报》2015年第5期907-914,共8页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家"九七三"重点基础研究发展计划项目(2011CB302400);国家自然科学基金(61272026);北京市自然科学基金重点项目暨北京市教委科技发展计划重点项目(KZ201210009011)

摘　　要：为了对尺度和旋转变换下的纹理图像进行正确的分类,将Radon变换和V-系统相结合,提出一种纹理分类的算法.首先利用Radon变换将图像的旋转化为平移,再对Radon变换后的图像进行V-变换;利用V-系统的多小波特性,经过一系列的降采样分解过程得到图像在V-系统下的各层次能量表达,并将这些能量作为纹理图像的特征描述.由于V-系统的多小波特性以及Radon变换对旋转的消除,使得文中的特征描述在图像的放缩和旋转变换下有较强的鲁棒性.在通用纹理数据库中的纹理分类实验结果表明了该算法的优越性能.To classify the scaled and rotated texture images correctly, this paper proposes a new algorithm for texture classification by combining Radon transform and the V-system. We firstly use the Radon trans-form to convert the image rotation into the image translation, and then apply the V-transform on the image obtained after Radon transform. The energies of the image on different levels under the V-system are ex-pressed by performing a series of downsampling process due to the multi-wavelet characteristics of the V-system. These obtained energies are used as the texture feature description. The feature description method in this paper is robust to the image scaling and rotation because of the multi-resolution characteris-tics of the V-system and elimination of rotation by applying Radon transform. Results of the experiments conducted on the standard texture datasets show that the proposed algorithm provides superior performance.

关 键 词：V-系统 RADON变换 多小波 多分辨 纹理分类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]