Binary shape recognition based on an automatic morphological shape decomposition

The authors present a technique for translation-invariant binary convex polygon shape recognition based on a morphological shape decomposition. The triangular shape primitives from the decomposition of convex shapes are used as features for shape recognition. The shape primitives are smaller and simpler than the templates of shapes; thus they are more efficient in representing shapes for discrimination. Maximum entropy reduction is used as an optimization criterion for selecting features from among the shape primitives at each node of a decision tree. Experiments on the classification of ten classes of noisy polygon shapes, where five replications per class were used for training and fifty replications per class were used for testing, achieved a recognition rate of 98.80% on the test set