Development of yConvex hypergraph model for contour-based image analysis

To recognize and classify any connected region-of-interest (ROI) in a digital image, the shape descriptor of the ROI has to be defined in terms of its boundary characteristics. However, for certain types of regions, it is very difficult to exactly trace the contour and none of the existing techniques satisfactorily work. In this paper, yConvex hypergraph model (yCHG) of digital image is introduced. It is a generalization of yConvex region decomposition [1] and represents any connected region as a finite set of disjoint yConvex hyperedges (yCHE). Each yCHE is a yConvex region, whose contour can be tracked in a deterministic way. Based on this hypergraph model (yCHG), an algorithm is presented for contour-based shape representation. Representations of contours of individual yCHEs are obtained independently and combined to provide a representation for the whole shape. This representation is rotation and scale invariant. The proposed algorithm works correctly on any connected region including those that can not be satisfactorily handled by any of the existing techniques.