Texture synthesis with tandem genetic algorithms using nonparametric partially ordered Markov models

In this paper we describe a solution to the problem of synthesizing textures. We use a pair of genetic algorithms to create fast one-pass generating algorithms for five black-and-white textures. This is done using only examples of those textures as input. The key to success is the use of a pair of genetic algorithms and a special structure called a foot pattern. The first genetic algorithm locates a foot pattern, a set of pixel locations containing important structural information about the texture, in essence a point of view from which the example texture looks relatively non-random. The foot pattern is a kind of basic texture element or texel. The second genetic algorithm then uses this texel as the core of a fitness function that compares two textures so as to tell when one “looks like” the other. With this “looks like” fitness function available, the second genetic algorithms synthesizes a non-parametric partially ordered Markov model for the example texture. The genetic algorithms used are themselves quite standard, but their pairing and the fitness functions used yield a breakthrough in black-and-white texture synthesis. Extending these techniques to gray scale and colored textures is possible, but suffers from combinatorial explosion. Suggestions on overcoming the difficulties of such extension appear in the discussion of future work