Edge potential functions (EPF) and genetic algorithms (GA) for edge-based shape matching of visual objects

Edges are known to be a semantically rich represen- tation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital im- ages by combining the concept of edge potential functions (EPF) with a powerful matching tool based on genetic algorithms (GAs). EPFs can be easily calculated starting from an edge map and pro- vide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies.