A near optimal acceptance-rejection algorithm for exact cross-correlation search

We describe a fast algorithm that searches for the k most likely locations of a template in an image according to the standard normalized correlations criterion. The algorithm is exact; it always finds the best matches. Its speed is achieved by utilizing an acceptance-rejection pruning scheme, applied to easily computed bounds on the normalized correlation values. Previously proposed rejection schemes require a rejection threshold that has to be provided or estimated from the data. Our algorithm does not use such thresholds explicitly, but performs as well as if the perfect rejection threshold is known.