The statistical approaches such as texture spectrum and local binary pattern methods have been discussed in this paper. The features are extracted by the computation of LBP and Texture Spectrum histogram. A combined approach of LBP with texture spectrum is also proposed further. Experiments of texture feature extraction, classification of textures and similarity-based image-to-image matching ar...

We propose an algorithm for texture segmentation based on a divide-and-conquer strategy of statistical modeling. Selected sets of Gaussian clusters, estimated via Expectation Maximization on the texture features, are grouped together to form composite texture classes. Our cluster grouping technique exploits the inherent local spatial correlation among posterior distributions of clusters belongi...

This paper conducts an empirical evaluation of the MPEG-7 texture descriptors and the LBP (Local Binary Pattern) operator. The experiment involves 319 textures from the Outex texture database, which makes it one of the largest experiments found in the literature, if not the largest, in terms of the number of texture classes. The descriptors are evaluated with respect to retrieval performance an...

The paper presents a genetic algorithm based methodology for learning a set offeature detectors for texture discrimination. This methodology is implemented inJo a vision system that learns to classify noisy examples of different texture classes by evolving populations of simple and texture-specific local spatial feature detectors.

In this paper a new algorithm to detect texture defects of ceramic tiles is proposed. The proposed algorithm has two stages: feature extraction and inspection. In the feature extraction stage, the parameters of proposed algorithm is determined using one (few) reference ceramic tile(s) with no defect. At first, the colors of the texture are clustered to form a model of texture colors. Then, each...

The classical TV (Total Variation) model has been applied to gray texture image denoising and inpainting previously based on the non local operators, but such model can not be directly used to color texture image inpainting due to coupling of different image layers in color images. In order to solve the inpainting problem for color texture images effectively, we propose a non local CTV (Color T...

This paper presents generalizations to the gray scale and rotation invariant texture classification method based on local binary patterns that we have recently introduced. We derive a generalized presentation that allows for realizing a gray scale and rotation invariant LBP operator for any quantization of the angular space and for any spatial resolution, and present a method for combining mult...

This work addresses the increasing demand for a sensitive and user-friendly iris based authentication system. We aim at reducing False Rejection Rate (FRR). The primary source of high FRR is the presence of degradation factors in iris texture. To reduce FRR, we propose a feature extraction method robust against such adverse factors. Founded on local and global variations of the texture, this me...

This paper introduces an innovative method, Dynamic Local Feature Analysis (DLFA), for human face recognition. In our proposed method, the face shape and the facial texture information are combined together by using the Local Feature Analysis (LFA) technique. The shape information is obtained by using our proposed adaptive edge detecting method that can reduce the effect on different lighting c...

Liver cancer leads to more number of human deaths nowadays. Patient survival chances can be increased by early detection of the tumour. Texture analysis based on moment features for CT liver scan images is proposed here. The texture feature is extracted by local binary pattern and statistical features are extracted by Legendre moments. This communication presents a comparative analysis between ...