In this paper a combined statistical and structural approach has been employed for texture representation. A set of Texture Primitives has been suggested. These primitives are basically tested for the presence of texture by conducting a suitable statistical test called Nair’s test. The set of universal primitives are labeled as local descriptor and the frequency of occurrences of these primitiv...

Texture classification is one of the most studied and challenging problems in computer vision. A key requirement of successful texture classification algorithms is their ability to quantify the complex nature and diversity of real world textures. Recent developments in automatic texture classification have demonstrated the effectiveness of modeling texture elements as cluster centers of respons...

Introduction: A major problem facing the patients with chronic liver diseases is the diagnostic procedure.  The conventional diagnostic method depends mainly on needle biopsy which is an invasive method. There  are  some  approaches  to  develop  a  reliable  noninvasive  method  of  evaluating  histological  changes  in  sonograms. The main characteristic used to distinguish between the normal...

Recently, the research towards Brodatz database for texture classification done at considerable amount of study has been published, the effective classification are vulnerable towards for training and test sets. This study presents the novel texture classification method based on feature descriptor, called spatial cooccurrence with discrete shearlet transformation through the LPboosting classif...

Texture analysis and classification are usual tasks in pattern recognition. Rock texture is a demanding classification task, because the texture is often non-homogenous. In this paper, we introduce a rock texture classification method, which is based on textural and spectral features of the rock. The spectral features are considered as some color parameters whereas the textural features are cal...

Texture can be considered as a repeating pattern of local variation of pixel intensities. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. One of the difficulties in texture classification was the lack of tools that characterize textures. Classification of textures has received attention during last few decades. As DCT works on gray leve...

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...

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...