Learning of Shape Models from Exemplars of Biological Objects in Images

Abstract

Generalized shape models of objects are necessary to match and identify an object in an image. Acquiring these kinds of models’ special methods is necessary as they allow learning the similarity pair-wise between the shapes. Their main concern is the establishment of point correspondences between two shapes and the detection of outlier. Known algorithm assume that the aligned shapes are quite similar in a way. But special problems arise if we align shapes that are very different, for example aligning concave to convex shapes. In such cases, it is indispensable to consider the order of the point-sets and to enforce legal sets of correspondences, otherwise the calculated distances are incorrect. We present our novel shape alignment algorithm which can also handle such cases. The algorithm establishes symmetric and legal one-to-one point correspondences between arbitrary shapes, represented as ordered sets of 2D-points and returns a distance measure which runs between 0 and 1.