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Fast normalized cross correlation based on adaptive multilevel winner update
Conference paper   Peer reviewed

Fast normalized cross correlation based on adaptive multilevel winner update

Shou-Der Wei and Shang-Hong Lai
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.4810 LNCS, pp.413-416
2007

Abstract

Fast algorithms Multi-level successive elimination Normalized cross correlation Pattern matching Winner update strategy
In this paper we propose a fast normalized cross correlation (NCC) algorithm for pattern matching based on combining adaptive multilevel partition with the winner update scheme. This winner update scheme is applied in conjunction with an upper bound for the cross correlation derived from Cauchy-Schwarz inequality. To apply the winner update scheme, we partition the summation of cross correlation into different levels with the partition order determined by the gradient energies of the partitioned regions in the template. Thus, this winner update scheme can be employed to skip the unnecessary calculation. Experimental results show the proposed algorithm is very efficient for image matching under different lighting conditions. © Springer-Verlag Berlin Heidelberg 2007.

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