From a modern Bayesian point of view, the classic Julesz random-dot stereogram is a cue-conflict stimulus: Texture cues specify an unbroken, unslanted surface, in conflict with any variation in depth specified by binocular disparity. We introduce a new visual texture-the starry night texture (SNT)--that is incapable of conveying slant, depth edges, or texture boundaries, in a single view. For S...

This paper introduces a nonparametric algorithm for bootstrapping a stationary random field and proves certain consistency properties of the algorithm for the case of mixing random fields. The motivation for this paper comes from relating a heuristic texture synthesis algorithm popular in computer vision to general nonparametric bootstrapping of stationary random fields. We give a formal resamp...

This paper presents a simple, novel, yet very powerful approach for robust rotation-invariant texture classification based on random projection. The proposed sorted random projection maintains the strengths of random projection, in being computationally efficient and low-dimensional, with the addition of a straightforward sorting step to introduce rotation invariance. At the feature extraction ...

The bidirectional texture function (BTF) is the recent most advanced representation of visual properties of material surface. It specifies its appearance due to varying spatial, illumination, and viewing conditions. Corresponding enormous BTF measurements require compact mathematical representation for visual fidelity preserving compression. We present a novel BTF model based on a set of underl...

The methods for the extraction of the main texture properties and for the subsequent texture synthesis are proposed based on the formal definitions of the essential statistical properties and the notion of texture in terms of pairwise and singleton signal interactions. The iterative synthesis procedure is a successive approximation in the space of the selected statistical properties towards the...

How should one model a texture? Statistically? Structurally? Or a bit of both? In this paper, we propose a method to help one decide. We assume a texture contains a random and a structured component, then estimate the proportion of energy in each component using a new algorithm. The energy proportions indicate whether one texture component dominates or an even mix exists. We implemented our alg...

We present a method for analyzing and resynthesizing inhomogeneously textured regions in images for the purpose of advanced compression. We first define image blocks in a way that they cover regions with homogenious texture. These blocks are transformed each in turn. For the transform we use the so called Principle Component Analysis. After the transform into the new domain we statistically ana...

This contribution is concerned with texture synthesis by example, the process of generating new texture images from a given sample. The Random Phase Noise algorithm presented here synthesizes a texture from an original image by simply randomizing its Fourier phase. It is able to reproduce textures which are characterized by their Fourier modulus, namely the random phase textures (or micro-textu...

Two novel models for texture representation using parametric compound random field models are introduced. These models consist of a set of several sub-models each having different characteristics along with an underlying structure model which controls transitions between them. The structure model is a two-dimensional probabilistic mixture model either of the Bernoulli or Gaussian mixture type. ...