Abstract
The main aim of the reported work is to solve the registration problem for recognition purposes. We introduce two new evolutionary algorithms (EA) consisting of population-based search methods, followed by or combined with a local search scheme. We used a variant of the Firefly algorithm to conduct the population-based search, while the local exploration was implemented by the Two-Membered Evolutionary Strategy (2M-ES). Both algorithms use fitness function based on mutual information (MI) to direct the exploration toward an appropriate candidate solution. A good similarity measure is the one that enables us to predict well, and with the symmetric MI we tie similarity between two objects A and B directly to how well A predicts B, and vice versa. Since the search landscape of normalized mutual information proved more amenable for evolutionary computation algorithms than simple MI, we use normalized mutual information (NMI) defined as symmetric uncertainty. The proposed algorithms are tested against the well-known Principal Axes Transformation technique (PAT), a standard evolutionary strategy and a version of the Firefly algorithm developed to align images. The accuracy and the efficiency of the proposed algorithms are experimentally confirmed by our tests, both methods being excellently fitted to registering images.
Highlights
Image registration is one of the well-known techniques belonging to the computer vision field [1,2,3].In the last few years, nature-inspired algorithms and metaheuristics have been used to address the image registration problem, becoming a solid alternative to direct optimization methods
We briefly describe the variant of the Firefly algorithm used for solving the binary image registration task introduced in [12]
The memetic algorithms (MA) are optimization methods in which the evolutionary process is enhanced with deterministic, heuristics or other local search techniques, which reduce the probability of premature convergence
Summary
Image registration is one of the well-known techniques belonging to the computer vision field [1,2,3]. The aim of the research work presented in this paper is to accurately register binary images using evolutionary search techniques. The aim is to develop a population-based evolutionary optimization model, in which the individuals evolve toward the chromosome corresponding to the given target image. A two-stage hybrid technique, that involves a population-based Firefly search and a variant of. A variant of Firefly technique [12] computes a “good” candidate solution, i.e., a chromosome whose fitness is larger than a certain threshold value. The Firefly search technique directs the exploration in an appropriate direction, the 2M-ES algorithm is used to compute an optimal solution.
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