Template Matching in Digital Images Using a Compact Genetic Algorithm With Elitism and Mutation

Abstract

The emCGA is a new extension of the compact genetic algorithm (CGA) that includes elitism and a mutation operator. These improvements do not increase significantly the computational cost or the memory consumption and, on the other hand, increase the overall performance in comparison with other similar works. The emCGA is applied to the problem of object recognition in digital images. The objective is to find a reference image (template) in a landscape image, subject to distortions and degradation in quality. Two models for dealing with the images are proposed, both based on the intensity of light. Several experiments were done with reference and landscape images, under different situations. The emCGA was compared with an exhaustive search algorithm and another CGA proposed in the literature. The emCGA was found to be more efficient for this problem, when compared with the other algorithms. We also compared the two proposed models for the object. One of them is more suitable for images with rich details, and the other for images with low illumination level. Both models seem to perform equally in the presence of distortions. Overall, results suggested the efficiency of emCGA for template matching in images and encourages future developments.

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

“…Te most interesting areas of application of compact optimisation in the discrete domain include the Travelling Salesman Problem (TSP) [224,261]; determining minimum set primers in Polymerase Chain Reaction (PCR) [262]; task scheduling in grid computing environments [263]; protein folding [264]; object recognition [265,266]; soft decision decoding [267,268]; minimising the number of coding operations required in multicast based on network coding [222]; estimating the parameters of the maximum log-likelihood function of a frst-order moving average model MA [269] and a mixed model ARMA (1, 1) [223]; optimising the aggregation of multiple similarity measures to obtain a single similarity metric for ontology matching [270]; optimising ontology alignment [271]; designing multiple input multiple output wireless communication systems [272].…”

Multiple rules with game theoretic analysis for flexible flow shop scheduling problem with component altering times

“…Rimcharoen et al (2006) applied a moving average technique to update the probabilistic vector to CGA that enabled the CGA to require fewer evaluations and could achieve a higher solution quality. Also, the mutation operation and some elitism-based schemes were used in Ahn and Ramakrishna (2003), Da Silva et al (2010) and Gallagher et al (2004). To our knowledge, CGA is totally a new method in the production scheduling field.…”

“…Secondly, design an adaptive elite inheritance strategy, which is different from the elite persistence strategies presented in Ahn and Ramakrishna (2003), Da Silva et al (2010) and Gallagher et al (2004). The strategy proposed in this paper is triggered spontaneously when the evolutionary stagnation is met, inheriting the optimum individual that ever got in the previous generations one time to help the evolution process escaping from the local optimal and moving forward.…”

An Improved Artificial Bee Colony Algorithm for the Object Recognition Problem in Complex Digital Images Using Template Matching

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