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.

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In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) model for synthesising and recognising texture. The model has the ability to capture the characteristics of a wide variety of textures, varying from the structured to the stochastic. For texture synthesis, we use our own novel multiscale approach, incorporating local annealing, allowing us to use large neighbourhood systems to model some complex textures. We show how we are able to manipulate the statistical order of our high dimensional model without over compromising the integrity of the representation. Also by varying the statistical order of our model we are able to optimise it for the unsupervised recognition of textures with respect to textures that have not been modelled.

Simulating bleeding in a virtual reality surgical simulator is an important task that still has not found a visually appealing solution. Bleeding in a simulator not only tests a surgeon to deal with critical issues, but also affects the environment by obscuring the view in which the surgeon has to operate. For any virtual reality surgical simulator, bleeding has to be treated, while at the same time the bleeding has to be responsive to any feedback that the surgeon may be conducing to the virtual reality environment. And all this has to be performed in real-time, i.e. at frame-rate. In this paper we present a methodology for solving this particular problem and show preliminary results of real-time visualization of bleeding in a dynamic virtual reality environment.