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An Improved Artificial Bee Colony Algorithm for the Object Recognition Problem in Complex Digital Images Using Template Matching
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Author(s): Chidambaram Chidambaram (Federal Technological University of Paraná (UTFPR), Brazil & Santa Catarina State University (UDESC), Brazil)and Heitor Silvério Lopes (Federal Technological University of Paraná (UTFPR), Brazil)
Copyright: 2012
Pages: 17
Source title:
Nature-Inspired Computing Design, Development, and Applications
Source Author(s)/Editor(s): Leandro Nunes de Castro (Mackenzie University, Brazil)
DOI: 10.4018/978-1-4666-1574-8.ch008
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Abstract
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.
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