Texture image segmentation method based on wavelet transform and neural networks

This paper presents an effective texture image segmentation algorithm by using wavelet decomposition and band-pass neural networks. This approach is applied to segment a random texture image into several homogeneous areas. The basic idea of proposed method is first decomposing an original image into several filtered images which contain information in different orientation and frequency ranges, and these filtered images are of the same size as the original images. Then the zero-crossing transformation is applied to all these filtered subimages. The texture features are extracted by calculating the energy, mean, variance and co-occurrence matrix of the small window in the filtered subimages. Then the feature vector pyramid are built of reduced-resolution versions of these arrays. By using band-pass neural networks in the pyramid linking process, the child can be linked to its most similar parent, at same time, the robustness of the system and the ability of noise resistant are improved a lot. The validity of this method will be verified by several numerical examples