Pattern Recognition
Object detection and experimental designs
Computer Vision, Graphics, and Image Processing
A note on binary template matching
Pattern Recognition
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
Digital Picture Processing
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
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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.