A survey of image registration techniques
ACM Computing Surveys (CSUR)
Fast template matching using bounded partial correlation
Machine Vision and Applications
Robust Real-Time Face Detection
International Journal of Computer Vision
Real-Time Pattern Matching Using Projection Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting inter-frame correlation for fast video to reference image alignment
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
A fast motion estimation algorithm based on the block sum pyramid
IEEE Transactions on Image Processing
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
A multilevel successive elimination algorithm for block matching motion estimation
IEEE Transactions on Image Processing
A fast globally optimal algorithm for template matching using low-resolution pruning
IEEE Transactions on Image Processing
Fast block matching algorithm based on the winner-update strategy
IEEE Transactions on Image Processing
Successive elimination algorithm for motion estimation
IEEE Transactions on Image Processing
A novel four-step search algorithm for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
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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.