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Fast template matching based on normalized cross correlation with adaptive multilevel winner update
Journal article   Peer reviewed

Fast template matching based on normalized cross correlation with adaptive multilevel winner update

Shou-Der Wei and Shang-Hong Lai
IEEE Transactions on Image Processing, Vol.17(11), pp.2227-2235
2008

Abstract

Fast algorithms Multilevel successive elimination Normalized cross correlation Pattern matching Winner update strategy
In this paper, we propose a fast pattern matching algorithm based on the normalized cross correlation (NCC) criterion by combining adaptive multilevel partition with the winner update scheme to achieve very efficient search. 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 in an efficient way, 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 in conjunction with the upper bound for NCC can be employed to skip unnecessary calculation. Experimental results show the proposed algorithm is very efficient for image matching under different lighting conditions. © 2008 IEEE.

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