利用小变换和特征加权进行纹理分割

为了提高纹理图象分割的边缘准确性和区域一致性以及降低分割错误率，提出了一种基于小波变换的利用特征加权来进行纹理分割的方法。该方法包括特征提取、预分割和后分割3个阶段，其中，特征提取在金字塔结构小小以变换的基础上进行；预分割利用均人矣类算法来对原始图象进行初步的分割；后分割则根据预分割的结果对特征进行加权，然后利用最小距离分类器来实现图象的最后分割。与传统的方法相比，该方法在分割错误率、边缘准确性以及区域一致性等方面均有明显的改善。

Texture Segmentation with Wavelet Transform and Feature Weighting

To improve the accuracy of boundary locations and region homogeneity as well as to reduce the error rate in texture image segmentation, a novel approach based on wavelet-transform and using feature weighting is proposed in this paper. This new technique contains three consecutive stages: feature extraction, pre-segmentation and post-segmentation. In the feature extraction stage, texture features are extracted by using the pyramid-structured wavelet transform. The original image is then segmented initially using the means clustering algorithm in the pre-segmentation stage. According to the pre-segmentation results, the extracted features are weighted and the pre-segmented image is further processed with a minimum distance classifier in the post-segmentation stage to finally get the segmented image. All technical points are clearly described and presented in detail. Some segmentation experiments with different Brodatz's texture images are performed to test the performance of the new technique and are also included. Compared with a typical traditional method, the present approach shows visible improvements both in diminishing segmentation error, and in increasing boundary precision and region harmony.