Article

Fast Template Matching Based on Normalized Cross Correlation With Adaptive Multilevel Winner Update

Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu
IEEE Transactions on Image Processing (impact factor: 3.04). 12/2008; DOI:10.1109/TIP.2008.2004615
Source: IEEE Xplore

ABSTRACT 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.

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Keywords

algorithm
 
conditions
 
different levels
 
efficient search
 
efficient way
 
Experimental results
 
fast pattern
 
gradient energies
 
normalized
 
partition order
 
proposed algorithm
 
unnecessary calculation
 
upper
 

Shou-Der Wei