ÐThis article presents a mathematical definition of textureÐthe Julesz ensemble …h†, which is the set of all images (defined on Z) that share identical statistics h. Then texture modeling is posed as an inverse problem: Given a set of images sampled from an unknown Julesz ensemble …h †, we search for the statistics h which define the ensemble. A Julesz ensemble …h† has an associated probability...

In this paper we propose a novel feature extraction scheme for texture classi cation, in which the texture features are extracted by a two-level hybrid scheme by integrating two statistical techniques of texture analysis. In the rst step, the low level features are extracted by the Gabor lters, and they are encoded with the feature map indices using the Kohonen's SOFM algorithm. In the next ste...

We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a Gaussian-Markov random fields (GMRF) model. Unlike a GMRF-based approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The secon...

The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most exi...

This article proposes a generative image model, which is called ‘‘primal sketch,’’ following Marr’s insight and terminology. This model combines two prominent classes of generative models, namely, sparse coding model and Markov random field model, for representing geometric structures and stochastic textures, respectively. Specifically, the image lattice is divided into structure domain and tex...

This article proposes a generative image model, which is called “primal sketch,” following Marr’s insight and terminology. This model combines two prominent classes of generative models, namely, sparse coding model and Markov random field model, for representing geometric structures and stochastic textures respectively. Specifically, the image lattice is divided into structure domain and textur...

Markov/Gibbs random elds have been used for posing a variety of computer vision and image processing problems. Many of these problems are then solved using a simulated annealing type of method which involves the varying of the \temperature," a scale parameter for the model. In this paper we analyze the eeect of temperature on random eld texture patterns. We obtain new results relating structure...

While there is a considerable history of work on visual texture, the definition of texture is still imprecise. However, it is generally agreed that a texture is spatially homogeneous and contains repeated visual patterns. In synthetic textures, such as horizontal lines, vertical lines or a checkerboard, the basic structure is repeated exactly. In natural textures, such as grass, wood, sand or r...

This paper presents a novel fast model-based algorithm for realistic multispectral BTF texture modelling potentially capable of direct implementation inside the graphical card processing unit. The algorithm starts with range map estimation of the BTF texture followed by the spectral and spatial factorisation of an input multispectral texture image. Single orthogonal monospectral band-limited fa...

Local Parameter Histograms (LPH) based on Gaussian Markov random fields (GMRFs) have been successfully used in effective texture discrimination. LPH features represent the normalized histograms of locally estimated GMRF parameters via local linear regression. However, these features are not rotation invariant. In this paper two techniques to design rotation invariant LPH texture descriptors are...