【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.更多还原

【Abstract】 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 transform 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 expressed 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 characteristics 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.更多还原