Efficient NCC-based image matching based on novel hierarchical bounds

Abstract

In this paper, we propose a fast image matching algorithm based on the normalized cross correlation (NCC) by applying the winner-update strategy in conjunction with the novel hierarchical bounds of cross correlation. We derive a novel upper bound for the cross-correlation of image matching based on the lower bound of sum of square difference (SSD), which is derived in the Walsh-Hadamard domain because of its nice energy packing property. Applying this upper bound with the winner update search strategy can skip unnecessary calculation, thus significantly reducing the computational burden of NCC-based pattern matching. Experimental results show the proposed algorithm is very efficient for NCC-based image matching under different lighting conditions and noise levels. © 2009 Springer-Verlag Berlin Heidelberg.