| Research Abstract |
In this study, highly accurate texture image segmentation methods by using genetic algorithms and neural networks have been inspected.
Genetic algorithm has attracted much attention because of its efficiency to large-scale, high-complexity optimization problems. In this study, considering segmentation of an unstationary texture image an optimization problem, we proposed a highly accurate texture segmentation method by applying genetic algorithms. To deal with the problem of convergence in genetic algorithms, a new approach was proposed to improve the convergence by using self-organized neural networks. And a novel pre-processing method was also proposed to extract texture features from highly unstationary texture images by using wavelet transformation.
Multi-resolution image processing decomposes an original image into a set of images with different resolution for image analysis and has been widely by many image analysis methods up to now. As to texture image segmentation, methods have been proposed by constructing a pyramid with multi-resolution images and searching parent-son relationship between image pixels at an upper level and a lower level. However, this method can not be applied to image with complex texture patterns because it only uses image intensity features. In this study, a new texture image segmentation method was proposed. In this method, the original image is decomposed into small regions with same sizes: a pyramid is then constructed with feature vectors extracted from each region, and the parent-son relationship between feature vectors are determined using neural networks. And a new feature extraction method was proposed by extracting topological texture feature from a series of binary images.
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