Paper Details

The edge potential function (EPF) approach is a promising edge-based shape matching tool for visual targetrecognition, and describes the similarity between contours by means of a potential field. However, background noise intest images may degrade the accuracy of the EPF approach in the identification of target contours. Furthermore, thecomputational load of the EPF approach is usually heavy, thus limiting its use in online applications. To solve theseproblems, this paper proposes a new shape matching tool based on atomic potential function (APF). The APF approachreduces the effects of background noise by introducing the concept of atom potential to the generation of potential fields.Moreover, in our proposed APF approach, the potential field is calculated using the contour extracted from a predefined target template rather than contours extracted from test images. Following the calculation of the potential field,the derived potential field is transformed to match the contours extracted from the test images. The search process for thetransformation that matches the contours most closely is modeled as an optimization problem solved by a modifiedversion of the artificial bee colony (ABC) algorithm – the internal feedback ABC (IF-ABC). Compared to theconventional ABC algorithm, IF-ABC effectively avoids premature convergence and significantly improves convergencespeed. Experimental results verify the feasibility and efficiency of our proposed APF approach by comparing it with thetraditional EPF method.

Mr. B. Chandrashaker Reddy, B.Sainath Reddy, B. Rohith, K. Srikanth, “SHAPE RECOGNITION USING ARTIFICIAL BEE COLONY OPTIMIZATION”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 4, pp. 160-167, April 2017.