Pattern recognition of strong graphs based on possibilistic c-means and k-formulae matching

A new graph matching approach based on 1D information is presented. Each node of the matched graphs represents a fuzzy region (fuzzy segmentation step). Each couple of nodes is linked by a relational histogram which can be assumed to the attraction of two regions following a set of directions. This attraction is computed by a continuous function, depending on the distance of the matched objects. Each case of the histogram corresponds to a particular direction. Then, relational graph computed from strong scenes are matched.