Template matching using an improved electromagnetism-like algorithm

Abstract

Template matching (TM) plays an important role in several image-processing applications such as feature tracking, object recognition, stereo matching, and remote sensing. The TM approach seeks for the best-possible resemblance between a subimage known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy. The simplest available TM method aims for the best-possible coincidence between the images through an exhaustive computation of the normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy). Recently, several TM algorithms that are based on evolutionary approaches have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the electromagnetism-like algorithm (EMO) is proposed to reduce the number of search locations in the TM process. The algorithm uses an enhanced EMO version, which incorporates a modification of the local search procedure to accelerate the exploitation process. As a result, the new EMO algorithm can substantially reduce the number of fitness function evaluations while preserving the good search capabilities of the original EMO. In the proposed approach, particles represent search locations, which move throughout the positions of the source image. The NCC coefficient, considered as the fitness value (charge extent), evaluates the matching quality presented between the template image and the coincident region of the source image, for a determined search position (particle). The number of NCC evaluations is also reduced by considering a memory, which stores the NCC values previously visited to avoid the re-evaluation of the same search locations (particles). Guided by the fitness values (NCC coefficients), the set of candidate positions are evolved through EMO operators until the best-possible resemblance is determined. The conducted simulations show that the proposed method achieves the best balance over other TM algorithms in terms of estimation accuracy and computational cost.