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.

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

MATTOCCIA, STEFANO;TOMBARI, FEDERICO;DI STEFANO, LUIGI
2009

Abstract

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.
2009
Lecture Notes in Computer Science
109
120
S. Mattoccia; F. Tombari; L. Di Stefano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/81408
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