In this paper, we applied the Artificial Bee Colony Algorithm (ABC) to the object recognition in the images. ABC is a new metaheuristics approach inspired by the collective and individual foraging behavior of honey bee swarm. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene, considering that it may be translated, scaled, rotated and/or partially occluded. This will result in location of the given reference image in the target landscape image. Results of the experiments with grayscale and color images show that the ABC is faster than other evolutionary algorithms and with comparable accuracy.
In this paper, the authors present an improved Artificial Bee Colony Algorithm (ABC) for the object recognition problem in complex digital images. The ABC is a new metaheuristics approach inspired by the collective foraging behavior of honey bee swarms. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene that may contain noise and changes in brightness and contrast. First, several search strategies were tested to find the most appropriate. Next, many experiments were done using complex digital grayscale and color images. Results are analyzed and compared with other algorithms through Pareto plots and graphs that show that the improved ABC was more efficient than the original ABC.
A wide range of approaches for 2D face recognition (FR) systems can be found in the literature due to its high applicability and issues that need more investigation yet which include occlusion, variations in scale, facial expression, and illumination. Over the last years, a growing number of improved 2D FR systems using Swarm Intelligence and Evolutionary Computing algorithms have emerged. The present work brings an up-to-date Systematic Literature Review (SLR) concerning the use of Swarm Intelligence and Evolutionary Computation applied in 2D FR systems. Also, this review analyses and points out the key techniques and algorithms used and suggests some directions for future research.Key words: Bio-inspired algorithms; Evolutionary algorithms; Face Recognition; Natural Computing; Optimization; Swarm algorithms
ResumoUma ampla gama de abordagens para sistemas de reconhecimento facial (FR) 2D pode ser encontrada na literatura devido a sua alta aplicabilidade e também por questões que necessitam de mais investigação, incluindo oclusão, variações de escala, expressão facial e iluminação. Nos últimos anos, um número crescente de sistemas de FR 2D usando algoritmos de Inteligência de Enxame e Computação Evolucionária surgiram. O presente trabalho traz uma Revisão Sistemática de Literatura (SLR) atualizada sobre o uso da Inteligência de Enxame e Computação Evolucionária aplicada em sistemas de FR 2D. Além disso, esta revisão analisa e aponta as principais técnicas e algoritmos utilizados e sugere alguns direcionamentos para pesquisas futuras.
In this paper, the authors present an improved Artificial Bee Colony Algorithm (ABC) for the object recognition problem in complex digital images. The ABC is a new metaheuristics approach inspired by the collective foraging behavior of honey bee swarms. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene that may contain noise and changes in brightness and contrast. First, several search strategies were tested to find the most appropriate. Next, many experiments were done using complex digital grayscale and color images. Results are analyzed and compared with other algorithms through Pareto plots and graphs that show that the improved ABC was more efficient than the original ABC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.