Abstract
Pattern Matching is a fundamental problem in computer vision, and image and video processing. Exhaustive-Search equivalent algorithms yield the same results as exhaustively searching all patterns in the image but significantly faster. In this paper, we propose a novel exhaustive-search equivalent algorithm that is combined with a number of state-of-art algorithms to provide a significantally faster alternative in the problem of finding nearest pattern according to a predefined distance measure. Our technique also shows high resilience to both blurring and JPEG compression types of noise. This is demonstrated in the paper with results from over 15 million runs for each compared algorithm.