Template matching (TM) plays an important role in several image processing applications. In a TM approach, it is sought the point in which it is presented the best possible resemblance between a sub-image 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 finds 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, based on evolutionary approaches, have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. On the other hand, bio-inspired computing has demonstrated to be useful in several application areas. Over the last decade, new bio-inspired algorithms have emerged with applications for detection, optimization and classification for its use in image processing. In this chapter, the Social Spider Optimization (SSO) algorithm is presented to reduce the number of search locations in the TM process. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. The algorithm considers two different search individuals (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In the proposed approach, spiders represent search locations which move throughout the positions of the source image. The NCC coefficient, used as a fitness value, evaluates the matching quality presented between the template image and the coincident region of the source image, for a determined search position (spider). The number of NCC evaluations is reduced by considering a memory which stores the NCC values previously visited in order to avoid the re-evaluation of the same search locations. Guided by the fitness values (NCC coefficients), the set of encoded candidate positions are evolved using the SSOoperators until the best possible resemblance has been found. Conducted simulations show that the proposed method achieves the best balance over other TM algorithms, in terms of estimation accuracy and computational cost.
ASJC Scopus subject areas
- Artificial Intelligence