Texture image segmentation using a new descriptor and mathematical morphology

I n this paper we present a new texture descriptor based on the shape operator defined in differential geometry. Then we describe the texture feature analysis process based on the spectral histogram. After that we describe a new algorithm for texture segmentation using this descriptor, statistics based on the spectral histogram, and mathematical morphology. Many results are presented to illustrate the effectiveness of our approach. Texture image segmentation is a fundamental problem i n computer vision with a wide variety of applications. The texture can be regarded as a similarity grouping in an image. The local sub-pattern properties give rise to the perceived lightness, uniformity, density, roughness, regularity, linearity, frequency, phase, directionality, coarseness, randomness, fineness, smoothness, granulation, etc. Because texture is regarded as a rich of source of visual information, it is difficult to define the properties that can be used effectively to characterize all textures and to find a set of properties that can be used to distinguish textures found in a given image. And it is also difficult to determine the texture region boundary accurately because the texture is a region property rather than a point property. The common approaches to solve the textured image segmentation problem can be classified to be either supervised or unsupervised algorithm based on whether the number of textures contained in the image is known in advance or not. The typical methods are region growing, estimation theory based on maximum likelihood, split-and-merge, Bayesian classification, probabilistic relaxation, clustering, the Mumford-Shah model, etc. In this paper, we choose the spectral histograms as the texture feature and adopt an effective computing method to evaluate the similarity between histograms. Spectral histogram consists of histograms of response images of chosen filters. It can capture local spatial patterns through filtering and global patterns through histograms and constraints among different filters compared with the above texture analysis methods. Based on the determined texture features, we can obtain the initial segmentation result. Skeleton extracting algorithm based on mathematical morphology is applied to determine the texture region