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.

Abstract-In this paper, we present a robust image alignment algorithm based on matching of relative gradient maps. This algorithm consists of two stages; namely, a learning-based approximate pattern search and an iterative energy-minimization procedure for matching relative image gradient. The first stage finds some candidate poses of the pattern from the image through a fast nearestneighbor search of the best match of the relative gradient features computed from training database of feature vectors, which are obtained from the synthesis of the geometrically transformed template image with the transformation parameters uniformly sampled from a given transformation parameter space. Subsequently, the candidate poses are further verified and refined by matching the relative gradient images through an iterative energy-minimization procedure. This approach based on the matching of relative gradients is robust against nonuniform illumination variations. Experimental results on both simulated and real images are shown to demonstrate superior efficiency and robustness of the proposed algorithm over the conventional normalized correlation method.Index Terms-Energy minimization, illumination variations, image alignment, image matching, industrial inspection, nearestneighbor search, robust image matching.

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Motion estimation (ME) plays an important role in video compression. Block-based ME has been adopted in most video compression standards due to its efficiency. In this paper, we propose a novel and fast block-based ME algorithm based on applying the modified winner-update scheme in conjunction with the adaptive partition order of macroblock. The partition order is determined from the block gradient distribution. Experimental results show the proposed algorithm achieves the optimal motion estimation very efficiently.