We present a novel Affine-Gradient based Local Binary Pattern (AGLBP) descriptor for texture classification. It is very hard to describe complicated texture using single type information, such as Local Binary Pattern (LBP), which just utilizes the sign information of the difference between pixel and its local neighbors. Our descriptor has three characteristics: 1) In order to make full use of t...

This paper presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns termed ‘uniform’ are fundamental properties of local image texture, and their...

In this paper, we propose a texture representation framework to map local texture patches into a low-dimensional texture subspace. In natural texture images, textons are entangled with multiple factors, such as rotation, scaling, viewpoint variation, illumination change, and non-rigid surface deformation. Mapping local texture patches into a low-dimensional subspace can alleviate or eliminate t...

Recently, we have developed a nonparametric approach to texture analysis, in which the distributions of simple texture measures based on local binary patterns and signed gray level differences are used to provide complementary information about the strurctural and stochastic properties of image texture. Very good performance has been obtained in various texture classification and segmentation p...

In this chapter we describe a unifying framework for local binary patterns and variants which we refer to as histograms of equivalent patterns (HEP). In presenting this concept we discuss some basic issues in texture analysis: the problem of defining what texture is; the problem of classifying the many existing texture descriptors; the concept of bag-of-features and the design choices that one ...

In this paper, we proposed a system of automatically classifying different types of dates from their images. Different dates have various distinguished features that can be useful to recognize a particular date. These features include color, texture, and shape. In the proposed system, a color image of a date is decomposed into its color components. Then, local texture descriptor in the form of ...

We analysed the temporal-frequency characteristics of two functional processes involved in orientation-based texture segregation: local orientation coding and subsequent orientation-contrast coding. Two texture images, in which each micropattern was rotated by 90 degrees, were alternated at various temporal frequencies. A micropattern was a second-derivative (D2) of a Gaussian that loses orient...

In this paper, we are proposing a novel texture descriptor, Structured Texton, for image representation. By exploring the local structure existed in the meaningful texture patterns, the structured textons are constructed by the nesting relationship between the different scale local extrema regions, which characterizes not only the local appearance features but also the geometric information. Fu...

Recently, the local binary pattern (LBP) has been widely used in texture classification. The conventional LBP methods only describe micro structures of texture images, such as edges, corners, spots and so on, although many of them show a good performance on texture classification. This situation still could not be changed, even though the multiresolution analysis technique is used in methods of...

Color texture descriptors have gained a lot of interest in computer vision applications. Methods for grayscale texture analysis have been extended to color images. This paper presents a new descriptor for color texture analysis based on the Local Mapped Pattern (LMP) methodology called Color Local Mapped Pattern (CLMP). For each color channel, C-LMP considers the sum of the differences of each ...