The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most exi...

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...

This paper presents a novel approach for visual scene representation, combining the use of quantized color and texture local invariant features (referred to here as visterms) computed over interest point regions. In particular we investigate the different ways to fuse together local information from texture and color in order to provide a better visterm representation. We develop and test our m...

We investigate the potential of mammographic parenchymal texture as a surrogate marker of the risk to develop Estrogen Receptor (ER) sub-type specific breast cancer. A case-control study was performed, including 118 cancer cases stratified by ER receptor status and 354 age-matched controls. Digital mammographic (DM) images were retrospectively collected and analyzed under HIPAA and IRB approval...

Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture image...

This study aims to develop a land cover texture-based classification scheme applicable for ALOS PALSAR imageries of the upper Marikina watershed acquired 2007 – 2010. From the raw dual polarization bands of HH+HV that has a ground resolution of 25m, additional bands HH/HV and NL was computed for surface texture normalization. The classification scheme was based on texture analysis using grey le...

In this paper we demonstrate that a genetic algorithm can be used to produce collections of pixel locations termed foot patterns useful for distinguishing between different types of texture images. The genetic algorithm minimizes the entropy of empirical samples taken with a particular foot pattern on a training image. The resulting low entropy foot patterns for several texture types are then u...

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...

This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag-of-words model to perform texture classification, thus learning and classification are carried out in the compressed domain. The p...