Shape recognition by a scale-invariant model

In this article, we considered the recognition of unknown shapes by maximum likelihood methods. The contour of a shape is represented by its centroidal profile, and it is fitted by a circular autoregressive model. Two different shape recognition problems are considered: the decision on the similarity of two unknown shapes, and the classification of an unknown shape as one of many known shapes. Maximum likelihood decision rules for these two cases are derived. The decision rules are invariant to translation, rotation, and size change after normalizing the estimates. The developed algorithms are applied to classify eight classes of machine parts and eight classes of aircraft shapes. For each class, 60 to 80 samples are generated by rotating and dilating the original shape. In the experiment, more than 98% of machine parts are classified correctly, and more than 97% of aircraft shapes are correctly classified. This result is better than previous model-based approaches. Partially supported by the National Science Foundation under the grant IRI-8809391.