Texture compression is a specialized form of image compression designed for storing texture maps in 3D computer graphics rendering systems. Texture compression is an important technique in graphics processing units (GPUs) for saving memory bandwidth. Unlike conventional image compression algorithms, texture compression algorithms are optimized for random access. Therefore, the variable-rate seq...

Texture classiication is an important area in the eld of texture analysis. In this paper, we propose a novel stochastic approach{multiresolution Markov Random Field (MRMRF) model to represent textures and a parameter estimation method based on Markov chain Monte Carlo method is proposed. The parameters estimated from the decomposed sub-bands can be used as features to classify textures. The cla...

A Hopf texture is a vacuum field configuration of isovector fields which is an onto map from the space as a large three sphere to the vacuum manifold S. We construct a Hopf texture with spherically symmetric energy density and discuss the topological charge. A Hopf texture collapses, and we study the collapse process numerically. In our simulations, it is clear that a Hopf texture does not deca...

We define a distance between textures for texture classification from texture features based on windowed Fourier filters. The definition of the distance relies on an interpretation of our texture attributes in terms of spectral density when the texture can be considered as a Gaussian random field. The distance between textures is then defined as a symmetrized Kullback distance which is a simple...

Efficient and high quality texture compression is important because computational powers are always limited, especially for embedded systems such as mobile phones. To access random texels instantly, down sampling and S3TC are the most commonly used methods due to the fixed compression ratio at every local region. However, the methods are content oblivious, which uniformly discard texel informat...

This paper looks at the nonparametric, multiscale, Markov Random Field (MRF) model and its application in classifying the MeasTex Test Suite. The MeasTex Test Suite is a standard by which various texture classification algorithms can be compared. Typically, todays texture classification algorithms have been based on supervised classification, whereby all the classification classes have been pre...

This article proposes a test of stationarity for random fields on a regular lattice motivated by a problem arising from texture analysis. Our approach is founded on the locally stationary two-dimensional wavelet (LS2W) process model for lattice processes which has previously been used for standard texture analysis tasks such as texture discrimination and classification. We propose two variants ...

Texture has been extensively studied and used in literature as it plays a very important role in human visual perception. Defining texture is not that easy; however some intuitive concepts can be defined about texture. Texture refers to the spatial distribution of grey-levels and can be defined as the repetition of one or several primitives in an image, in a deterministic or random way. Microte...

On electropolished steel at low current densities, morphology and texture of electrodeposited zinc were investigated. Zinc coating is consisted of hexagonal crystallites laid on each other to produce packets. These packets are of different sizes and are stacked in different orientations to construct a homogeneous coating on steel substrate. This coating does not have texture, i.e., it has a ran...

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...