Enhanced Low-Resolution Pruning for Fast Full-Search Template Matching

Gharavi-Alkhansari [1] proposed a full-search equivalent algorithm for speeding-up template matching based on L p -norm distance measures. This algorithm performs a pruning of mismatching candidates based on multilevel pruning conditions and it has been shown that, under certain assumptions on the distortion between the image and the template, it is faster than the other full-search equivalent algorithms proposed so far, including algorithms based on the Fast Fourier Transform. In this paper we propose an original contribution with respect to Gharavi-Alkhansari’s work that is based on the exploitation of an initial estimation of the global minimum aimed at increasing the efficiency of the pruning process.