identification based on faces is still a useful method for many applications and face recognition developing is an active research field. in this paper, a novel face identification method is proposed. the proposed method (bridle path on gabor phase (bpgp)) is based on extracting texture patterns from phases of the gabor wavelet. also, in order to describe the textures, a novel texture descripto...

Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature....

Texture is one of the fundamental image characteristics useful in computer vision tasks such as object recognition and scene analysis. Texture segmentation is one of the image analysis tasks. The prospect of texture segmentation depends on the choice of the texture description method and the segmentation procedure. In this paper, color-texture descriptors are proposed to represent the texture c...

Use of a single technique for the extraction of diverse features in a texture image usually shows limited capabilities for texture description. Texture features extracted using different techniques can be merged in an attempt to enhance their texture description capability. This paper explores the fusion of optimized moment and Gabor energy texture features. The Fisher linear discriminant analy...

A novel method of texture primitive description using morphological skeleton is proposed. The skeleton of an object has the property that it is reduced to one point when the structuring element used for the skeletonization is exactly homothetic to the object. This method applies this property. This method minimizes the number of pixels contained in the skeleton. If we assume that the texture is...

A model for probabilistic independent component subspace analysis is developed and applied to texture description. Experiments show it to perform comparably to a Gaussian model, and to be useful mainly for problems in which the detection of little occurring, highfrequency image elements is important.