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...

Amethod is proposed for filling in missing areas of degraded images through explicit structure reconstruction, followed by texture synthesis. The structure being reconstructed represents meaningful edges from the image, which are traced inside the artefact. The structure reconstruction step relies on different properties of the edges touching the artefact and of the areas between them, in order...

In Chinese calligraphy cao shu is regarded as a kind of free form art which differs from other styles greatly in its less constrained strokes and brush textures. In this paper we present a framework for synthesizing cao shu realistically. In our system, we adopt different brush texture patches (BTPs) collected from hand-written artworks to represent the solid and hollow strokes appearing in cao...

in this paper, a new method is presented for the detection of defects in random textures. in the training stage, the feature vectors of the normal textures’ images are extracted by using the optimal response of gabor wavelet filters, and their probability density is estimated by means of the gaussian mixture model (gmm). in the testing stage, similar to the previous stage,at  first, the feature...

texture image analysis is one of the most important working realms of imageprocessing in medical sciences and industry. up to present, different approacheshave been proposed for segmentation of texture images. in this paper, we offeredunsupervised texture image segmentation based on markov random field (mrf)model. first, we used gabor filter with different parameters’ (frequency,orientation) va...

This paper describes a simple novel compound random field model capable of realistic modelling the most advanced recent representation of visual properties of surface materials the bidirectional texture function. The presented compound random field model combines a non-parametric control random field with local multispectral models for single regions and thus allows to avoid demanding iterative...

In this paper, a new texture classification method is provided for dividing texture images into three classes: periodic, directional, and random. The method is based on the fact that for a directional texture image, the magnitudes of its Fourier spectrum will concentrate on a certain direction; for periodic, on several directions; and for random, spread out over all directions. To use this fact...

Vector field visualization generates an image to convey the information existing in the data. We use Markov Random Field texture synthesis methods to generate the visualization from a set of example textures. The examples textures are chosen according to the vector data for each pixel of the output. This leads to dense visualizations with arbitrary example textures.

Random eld models are able to synthesize a large variety of complex patterns with a small number of parameters. This paper discusses the use of a Gibbs random eld model as part of an image coding system. In particular, some semantic and perceptual attributes of this model are addressed.

This article presents a statistical theory for texture modeling. This theory combines ltering theory and Markov random eld modeling through the maximum entropy principle , and interprets and clariies many previous concepts and methods for texture analysis and synthesis from a uniied point of view. Our theory characterizes the ensemble of images I with the same texture appearance by a probabilit...