Jafari-Khouzani Kourosh, Soltanian-Zadeh Hamid
Radiology Image Analysis Laboratory, Henry Ford Health System, Detroit, MI 48202, USA.
IEEE Trans Image Process. 2005 Jun;14(6):783-95. doi: 10.1109/tip.2005.847302.
A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A kappa-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods.
提出了一种使用拉东变换和小波变换的新的旋转不变纹理分析技术。该技术利用拉东变换将旋转转换为平移,然后对结果应用平移不变小波变换来提取纹理特征。采用κ最近邻分类器对纹理模式进行分类。提出了一种找到拉东变换最佳投影数的方法。结果表明,提取的特征生成了一个有效的正交特征空间。还表明,所提出的特征提取了纹理模式的局部和方向信息。由于在拉东变换步骤中对像素值求和以生成投影,所提出的方法对加性白噪声具有鲁棒性。为了测试和评估该方法,我们使用了几组纹理以及不同的小波基。实验结果表明,与一些最近的纹理分析方法相比,所提出的方法具有优越性及其对加性白噪声的鲁棒性。