Recently, the orderless Bag-of-Words (BoW) approach has proven extremely popular and successful in texture classification tasks [1, 2, 3]. Due to its impressive computational efficiency and good texture discriminative property, the BoW-based approach LBP [1] has gained considerable attention. In order to employ the advantages of the method of Zhang et al. [3] in combining complementary local fe...

In many image processing applications, such as segmentation and classification, the selection of robust features descriptors is crucial to improve the discrimination capabilities in real world scenarios. In particular, it is well known that image textures constitute power visual cues for feature extraction and classification. In the past few years the local binary pattern (LBP) approach, a text...

Wavelet-based transform coding is well known for its utility in perceptual image compression. Psychovisual modeling has lead to a variety of perceptual quantization schemes, for efficient at-threshold compression. Successfully extending these models to supra-threshold compression, however, is a more difficult task. This work attempts to bridge the gap between at threshold modeling and supra-thr...

In this paper, an efficient local operator, namely the Local Quantization Code (LQC), is proposed for texture classification. The conventional local binary pattern can be regarded as a special local quantization method with two levels, 0 and 1. Some variants of the LBP demonstrate that increasing the local quantization level can enhance the local discriminative capability. Hence, we present a s...

High performance human identification using iris biometrics requires the development of automated algorithms for robust segmentation of the iris region given an ocular image. Many studies have shown that iris segmentation is one of the most crucial element of iris recognition systems. While many iris segmentation techniques have been proposed, most of these methods try to leverage gradient info...

The LBP operator is a theoretically simple yet very powerful method of analyzing textures. Through its recent extensions, it has been made into a really powerful measure of image texture, showing excellent results in terms of accuracy and computational complexity in many empirical studies. The LBP operator can be seen as a unifying approach to the traditionally divergent statistical and structu...

Morphological granulometry has been shown to be effective in a range of texture analysis applications. We describe an extension to the standard approach which allows truly local properties of the texture to be measured at each pixel. The result is a set of texture features which are analogous to those which could be measured for individual texture primitives, if they could be isolated. We demon...

In this paper, we propose and evaluate palmprint recognition method based on local Haralick features. The Haralick features are extracted from overlapping square subimages of a palmprint region of interest (ROI). A biometric template is composed of N m-component feature vectors, where N is the total number of overlapping subimages, and m is the number of local Haralick features per subimage in ...

Texture feature extraction is an important step in the facial expression recognition system. The traditional LBP method ignored the statistical characteristics of the texture change direction in the process of feature extraction, and we can extract more detailed texture information by the LDP method based on LBP, but the computational complexity is greatly increased. In order to extract more de...