Vector field visualization using Markov Random Field texture synthesis

Texture synthesis via a noncausal nonparametric multiscale Markov random field

Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesizing and capturing the characteristics of a wide variety of textures, from the highly structured to the stochastic. We use a multiscale synthesis algorithm incorporating local annealing to obtain larger realizations of texture visually indistinguishable from the training texture.

Enhanced Spot Noise for Vector Field Visualization

Spot noise is a technique for texture synthesis, which is very useful for vector field visualization. This paper describes improvements and extensions of the basic principle of spot noise. First, better visualization of highly curved vector fields with spot noise is achieved, by adapting the shape of the spots to the local velocity field. Second, filtering of spots is proposed to eliminate unde...

Near-Regular Texture Synthesis

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...

Texture Synthesis via a Non-parametric Markov Random Field

In this paper we present a non-causal non-parametric multiscale Markov random field (MRF) texture model that is capable of synthesising a wide variety of textures. The textures that this model is capable of synthesising vary from the highly structured to the stochastic type and include those found in the Brodatz album of textures. The texture model uses Parzen estimation to estimate the conditi...

Steerable texture synthesis for vector field visualization