Template matching of binary targets in grey-scale images: A nonparametric approach

摘要

This paper introduces a nonparametric similarity measure, based on the Kolmogorov-Smirnov (KS) statistic, to be used in template-matching problems where a target of binary characteristics is to be located in a grey-scale image. KS statistic yields the best-expected value for a binary-domain similarity measure if the threshold selection to binarize the image had been optimized to take into account the geometric constraints of the template; there is, however, no need to actually binarize the image. Some good properties of a KS-based similarity measure are exposed and compared with the corresponding properties of normalized correlation. A practical algorithm to implement a template matching procedure based on the KS statistic is shown, and its computing time is compared with normalized correlation. A KS-based similarity measure proves to be usually much faster computationally that normalized correlation. Finally, some experimental results are shown.