Texture analysis is one of the important as well as useful tasks in image processing applications. Many texture models have been developed over the past few years and Local Binary Patterns (LBP) is one of the simple and efficient approach among them. A number of extensions to the LBP method have been also presented but the problem remains challenging in feature vector generation and comparison....

As a representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in twoclass classification. However, when it is expanded to a multi-class classification problem, the precision of its discrimination may become worse. A main reason is an occurrence of overlapped distributions on the discriminant space built by Fisher criterion. I...

Extraction of the flooded areas from synthetic aperture radar (SAR) and especially TerraSAR-X data is one of the most challenging tasks in the flood management and planning. SAR data due to its high spatial resolution and its capability of all weather conditions makes a proper choice for tropical countries. Texture is considered as an effective factor in distinguishing the classes especially in...

Texture plays an important role in many image analysis applications. In this paper, we give a performance evaluation of color texture classification by performing wavelet scattering network in various color spaces. Experimental results on the KTH_TIPS_COL database show that opponent RGB based wavelet scattering network outperforms other color spaces. Therefore, when dealing with the problem of ...

This paper aims to improve the accuracy of texture classification based on extracting texture features using five different texture methods and classifying the patterns using a naïve Bayesian classifier. Three statistical-based and two model-based methods are used to extract texture features from eight different texture images, then their accuracy is ranked after using each method individually ...

In this paper we present a new wavelet domain technique for texture analysis and test of pattern replicability. The main property of the proposed features is that they measure texture quality along the most important perceptual dimensions. In other words, we quantify and classify textures according to their directionality, symmetry, regularity and type of regularity. After the feature extractio...

in this study, potential application of image texture analysis as a non-destructive method for automation and prediction of mechanical properties of carrot chips was investigated. samples were fried at different processing conditions and moisture content, colour parameters (i.e. l*, a*, b* and e) and mechanical properties (i.e. hardness and apparent modulus) were determined. hardness and appar...

This paper describes the use the granold texture representation for image texture classification. The granold uses two different parameterised monotonic mappings to transform an input image into a function on two dimensions that may be regarded as a surface. The nature of this surface is such that corners appear at positions where there are simultaneously large changes in the response of the mo...

The aim of the study in this research deals with the accurate texture classification and the image texture analysis has a voluminous errand prospective in real world applications. In this study, the texton co-occurrence matrix applied to the Broadatz database images that derive the template texton grid images and it undergoes to the discrete shearlet transform to decompose the image. The entrop...