<title>Hierarchical distributed template matching</title>

Abstract

We propose Hierarchical Distributed Template Matching, which reduces the computational cost of template matching, while maintaining the same reliability as conventional template matching. To achieve cost reduction without loss of reliability, we first evaluate the correlation of shrunken images in order to select the maximum depth of the hierarchy. Then, for each level of hierarchy, we choose a small number of template points in the original template and build a sparse distributed template. The locations of the template points are optimized, so that they yield a distinct peak in the correlation score map. Experimental results demonstrate that our method can reduce the computational cost to less than 1/10 that of conventional hierarchical template matching. We also confirmed that the precision is 0.6 pixels.