Academic Press, San Diego). One major problem is on how to estimate or recover the parameter values for a given procedural texture using the input texture image if the original parameter values are not available. In this paper, we propose a solution to this problem and present a genetic-based multiresolution parameter estimation approach.
The key idea of our approach is to use an efficient search method (a genetic-based search algorithm is used in this paper) to find appropriate values of the parameters for the given procedure. During the search process, for each set of parameter values, we generate a temporary texture image using the given texturing procedure; then we compare the temporary texture image with the given target texture image to check if they match. The comparison between two texture images is done by using a multiresolution MRF texture model. The search process stops when a match is found. The estimated values of the parameters for a given procedure are the values of the parameters to the procedure to generate a texture image that matches the target texture image. Experimental results are presented to demonstrate the success of our approach. Application of our parameter estimation approach to texture synthesis is also discussed in the paper.