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A novel shape of matching approach using modified artificial bee colony algorithm


Mohammad Ali Hamidi, Mojtaba Seyedzadegan


Vol. 16  No. 10  pp. 58-63


Image matching is one of fundamental importance in photogrammetry, remote sensing and computer vision. It has been used in 3D reconstruction, target tracking and other applications. Image matching aims to identify the correspondence between two different images of the same scene or objects in different poses, illumination conditions and environments. During the recent years, artificial bee colony (ABC) was proposed by scientists based on colony intelligence of bees, in order to resolve the complex problems artificial systems. The function of mutation operator in genetic algorithm lead to an extended search environment and discovery of a suitable result which equals with the best matching. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this work, ABC technique is exploited to tackle the shape matching problem with this aim to find the matching between two shapes represented via sets of contour points. In this paper, the combination of these two methods, leads to the creation of our suggested method entitled: Modified Artificial Bee Colony (MABC). Experimental results in image matching shows that our proposed novel method performs much better performance than other algorithms.


Shape matching, Machine vision, Image processing, Swarm intelligence, Bee colony, Mutation operator