Invariant planar shape recognition using dynamic alignment

A technique for classifying closed planar shapes is described in which a shape is characterized by an ordered sequence that represents the Euclidean distance between the centroid and all contour pixels of the shape. Shapes belonging to the same class have similar sequences, hence a procedure for classifying shapes is based on the degree of similarity between these sequences. In order to determine the similarity between sequences, a non-linear alignment process is developed to find the best correspondence between the sequences. Optimum alignment is obtained by expanding segments of the sequences to minimize a dissimilarity function between the sequences. Normalization with respect to scaling and rotation is described and an example illustrating the use of dynamic alignment for the classification of noisy shapes is presented.