Traditionally texture analysis is approached either by statistically evaluating the distribution of the pixels in a local neighbourhood or by filtering the image with a bank of filters that are applied to capture the changes in the spatial/frequency domain. The aim of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at...

In this paper, local distributions of low order Gaussian Markov Random Field (GMRF) model parameters are proposed as texture features for unsupervised texture segmentation. Instead of using model parameters as texture features, we exploit the variations in parameter estimates found by model fitting in local region around the given pixel. The spatially localized estimation process is carried out...

Methods based on local image features have recently shown promise for texture classification tasks, especially in the presence of large intra-class variation due to illumination, scale, and viewpoint changes. Inspired by the theories of image structure analysis, this paper presents a simple, efficient, yet robust descriptor namely local jet pattern (LJP) for texture classification. In this appr...

Local binary pattern (LBP) is an effective texture descriptor which has successful applications in texture classification and face recognition. Many extensions are made for conventional LBP descriptors. One of the extensions is dominant local binary patterns which aim at extracting the dominant local structures in texture images. The second extension is representing LBP descriptors in Gabor tra...

Many interesting real-world textures are inhomogeneous and/or anisotropic. An inhomogeneous texture is one where various visual properties exhibit significant changes across the texture’s spatial domain. Examples include perceptible changes in surface color, lighting, local texture pattern and/or its apparent scale, and weathering effects, which may vary abruptly, or in a continuous fashion. An...

A system is proposed which analyzes various textures statistically or structurally, depending on the nature of the textures. The system first describes a texture globally by both gray level first order statistics and edge statistics to roughly classify it. Next, the system extracts homogeneous gray level regions to try to describe the texture structurally. However, if too many regions are extra...

automatic analysis of human facial expressions is one of the challenging problems in machine vision systems. it has many applications in human-computer interactions such as, social signal processing, social robots, deceit detection, interactive video and behavior monitoring. in this paper, we develop a new method for automatic facial expression recognition based on facial muscle anatomy and hum...

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....

The local binary pattern (LBP) provides a simple and efficient approach to gray-scale and rotation invariant texture classification. However, the LBP operator thresholds P neighbors at the value of the center pixel in a local neighborhood and employs a P-bit binary pattern to encode only the signs of the differences between the gray values. Thus, the LBP operator discards some important texture...

Texture is one of the fundamental image characteristics useful in computer vision tasks such as object recognition and scene analysis. Texture segmentation is one of the image analysis tasks. The prospect of texture segmentation depends on the choice of the texture description method and the segmentation procedure. In this paper, color-texture descriptors are proposed to represent the texture c...