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We propose a novel algorithm, referred to as enhanced bounded correlation (EBC), that significantly reduces the number of computations required to carry out template matching based on normalized cross correlation (NCC) and yields exactly the same result as the full search algorithm. The algorithm relies on the concept of bounding the matching function: finding an efficiently computable upper bound of the NCC rapidly prunes those candidates that cannot provide a better NCC score with respect to the current best match. In this framework, we apply a succession of increasingly tighter upper bounding functions based on Cauchy-Schwarz inequality. Moreover, by including an online parameter prediction step into EBC, we obtain a parameter free algorithm that, in most cases, affords computational advantages very similar to those attainable by optimal offline parameter tuning. Experimental results show that the proposed algorithm can significantly accelerate a full-search equivalent template matching process and outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pattern matching is widely used in signal processing, computer vision, and image and video processing. Full search equivalent algorithms accelerate the pattern matching process and, in the meantime, yield exactly the same result as the full search. This paper proposes an analysis and comparison of state-of-the-art algorithms for full search equivalent pattern matching. Our intention is that the data sets and tests used in our evaluation will be a benchmark for testing future pattern matching algorithms, and that the analysis concerning state-of-the-art algorithms could inspire new fast algorithms. We also propose extensions of the evaluated algorithms and show that they outperform the original formulations. [ABSTRACT FROM AUTHOR]