Semiparametric skew-symmetric modeling of planar shapes

Shape modeling and template learning form an important area of research in image analysis. The paper addresses the problem from a novel viewpoint /sup s/ing a new class of semiparametric skew distributions. Given several realizations of a shape, we represent its template as a joint distribution of angle and distance from the centroid for all points on the boundary. The shape boundary may be arbitrary and irregular but simple. Its corresponding distribution is learned from the scattered data points of the available boundary realizations. We first obtained a bimodal distribution of the radii distances for given angles and subsequently synthesize the overall joint distributions according to some prior on the angles. We substantiate our proposed methodology with a number of examples.