We consider the problem of zone class$cation in document image processing. Document blocks are labelled as text or non-text using texture features derived from a feature based interaction map (FBIM), a recently introduced general tool for texture analysis [3, 41. The zone classijication procedure proposed is tested on the comprehensive document image database UW-I created at the University of W...

The Applications of Pattern recognition like wood classification, stone and rock classification problems, the major usage techniques ate different texture classification techniques. Generally most of the problems used statistical approach for texture analysis and texture classification. Gray Level Co-occurrence Matrices (GLCM) approach is particularly applied in texture analysis and texture cla...

In statistical model based texture feature extraction, features based on spatially varying parameters achieve higher discriminative performances compared to spatially constant parameters. In this paper we formulate a novel Bayesian framework which achieves texture characterization by spatially varying parameters based on Gaussian Markov random fields. The parameter estimation is carried out by ...

The aim of this chapter is to give experienced and new practitioners in image analysis and computer vision an overview and a quick reference to the “galaxy” of features that exist in the field of texture analysis. Clearly, given the limited space, only a corner of this vast galaxy is covered here! Firstly, a brief taxonomy of texture analysis approaches is outlined. Then, a list of widely used ...

In this study, an efficient feature fusion based technique for the classification of colour texture images in VisTex album is presented. Gray Level Co-occurrence Matrix (GLCM) and its associated texture features contrast, correlation, energy and homogeneity are used in the proposed approach. The proposed GLCM texture features are obtained from the original colour texture as well as the first no...

Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels i...

For the purpose of face recognition (FR), the new color local texture features, i.e., color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP), are being proposed. The proposed color local texture features are able to exploit the discriminative information derived from spatiochromatic texture patterns of different spectral channels within a certain local face region. This method...

Introduction: To explore diagnostic potential of computerize texture analysis methods in discrimination of the normal, benign and malignant ovarian lesions by CT scan imaging.   Materials and Methods: Ovarian CT image database consists of 10 normal, 10 benign and 3 malignant which were reported by radiologist and proven by clinical examinat...