Efficiency of template matching methods for Multiple-Point Statistics simulations

Almost all Multiple-Point Statistic (MPS) methods use internally a template matching method to select patterns that best match conditioning data. The purpose of this paper is to analyze the performances of ten of the most frequently used template matching techniques in the framework of MPS algorithms. Performance is measured in terms of computing efficiency, accuracy, and memory usage. The methods were tested with both categorical and continuous training images (TI). The analysis considers the ability of those methods to locate rapidly and with minimum error a data event with a specific proportion of known pixels and a certain amount of noise.

Experiments indicate that the Coarse to Fine using Entropy (CFE) method is the fastest in all configurations. Skipping methods are efficient as well. In terms of accuracy, and without noise all methods except CFE and cross correlation (CC) perform well. CC is the least accurate in all configurations if the TI is not normalized. This method performs better when normalized training images are used. The Binary Sum of Absolute Difference is the most robust against noise. Finally, in terms of memory usage, CFE is the worst among the ten methods that were tested; the other methods are not significantly different.