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 probability...

This paper presents a novel technique for texture modeling and synthesis using the random neural network (RNN). This technique is based on learning the weights of a recurrent network directly from the texture image. The same trained recurrent network is then used to generate a synthetic texture that imitates the original one. The proposed texture learning technique is very e cient and its compu...

We investigated the role of spatial arrangement of texture elements in three psychophysical experiments on texture discrimination and texture segregation. In our stimuli, oriented Gabor elements formed an iso-oriented and a randomly oriented texture region. We manipulated (1) the orientation similarity in the iso-oriented region by adding orientation jitter to the orientation of each Gabor; (2)...

This paper describes a method for seamless enlargement or editing of difficult colour textures containing simultaneously both regular periodic and stochastic components. Such textures cannot be successfully modelled using neither simple tiling nor using purely stochastic models. However these textures are often required for realistic appearance visualisation of many man-made environments and fo...

A large number of texture classification approaches have been developed in the past but most of these studies target gray-level textures. In this paper, supervised classification of color textures is considered. Several different Multispectral Random Field models are used to characterize the texture. The classifying features are based on the estimated parameters of these model and functions def...

This paper describes method for analysis of the texture created by retinal nerve fibers (RNF) via Markov Random Fields. The Causal Autoregressive Random (CAR) model is used to create a feature vector describing the changes in texture due to losses in RNF layer. It is shown that features based on CAR model can be used for discrimination between healthy and glaucomatous tissue using simple linear...

Motivation: The notion of texture is difficult to capture formally. Textures are usually the output of some random physical process wherein local structure is repeated seemingly at random and there is a lack of global structure. So, while the fur of a leopard is considered to be a texture, the entire leopard is not. This distinction is unavoidably arbitrary. We present an approach to texture re...

In statistical model based texture feature extraction, features based on spatially varying parameters achieve higher discriminative performances compared to spatially constant parameters. In this paper we formulate a novel Bayesian framework which achieves texture characterization by spatially varying parameters based on Gaussian Markov random fields. The parameter estimation is carried out by ...

Random and natural textures classification is still one of the biggest challenges in the field of image processing and pattern recognition. In this paper, texture feature extraction using Slant Hadamard Transform was studied and compared to other signal processing-based texture classification schemes. A parametric SHT was also introduced and employed for natural textures feature extraction. We ...