An unsupervised color texture synthesis-by analysis method is described. The texture is reproduced to appear perceptually similar to a given prototype by copying its statistical properties up to the second order. The synthesized texture is obtained at the output of a single-input three-output nonlinear system driven by a realization of a white Gaussian random field. Significant complexity reduc...

Traditionally, texture perception has been studied using artificial textures made of random dots or repeated shapes. At the same time, computer algorithms for natural texture synthesis have improved dramatically. We seek to unify these two fields through a psychophysical assessment of a particular computational model, providing insight into which statistics are most vital for natural texture pe...

We present a method for binary texture synthesis based on thresholds of Gaussian random fields. The method enables us to reproduce the average value and correlation function of the observed texture. The method is comparatively simple, and it seems to be effective for a wide class of random binary textures. In the paper we discuss properties of the method and illustrate its performance in the st...

the evolution of texture was discussed during the formation of ultra-fine and nano grains in a magnesium alloy severely deformed through accumulative back extrusion (abe). the microstructure and texture obtained after applying multiple deformation passes at temperatures of 100 and 250°c were characterized. the results showed that after single abe pass at 100°c an ultrafine/nano grained microstr...

Recent research in haptic systems has begun to focus on the generation of textures to enhance haptic simulations. Synthetic texture generation can be achieved through the use of stochastic modeling techniques to produce random and pseudo-random texture patterns. These models are based on techniques used in computer graphics texture generation and textured image analysis and modeling. The goal f...

Abstract How to establish the matching (or corresponding) between two different 3D shapes is a classical problem. This paper focused on the research on shape mapping of 3D mesh models, and proposed a shape mapping algorithm based on Hidden Markov Random Field and EM algorithm, as introducing a hidden state random variable associated with the adjacent blocks of shape matching when establishing H...

The problem that the Markov random field (MRF) model captures the structural as well as the stochastic textures for remote sensing image segmentation is considered. As the one-point clique, namely, the external field, reflects the priori knowledge of the relative likelihood of the different region types which is often unknown, one would like to consider only two-pairwise clique in the texture. ...

Gibbs random eld (GRF) models and co-occurrence statistics are typically considered as separate but useful tools for texture discrimination. In this paper we show an explicit relationship between co-occurrences and a large class of GRF's. This result comes from a new framework based on a set-theoretic concept called the \aura set" and on measures of this set, \aura measures". This framework is ...

We present a nonparametric Markov Random Field model for classifying texture in images. This model can capture the characteristics of a wide variety of textures, varying from the highly structured to the stochastic. The power of our modelling technique is evident in that only a small training image is required, even when the training texture contains long range characteristics. We show how this...

In this paper, we present a fast, simple and very powerful method for identifying human beings based on features of their iris texture. A very simple approach is presented to extract texture features of highly random iris texture on the contrary to current approaches that use complex mathematical description of the iris texture for feature extraction. The proposed method is tested with more tha...