Noninformation-preserving shape features at multiple resolution

The performance of any classification method is limited by the quality of the feature measurements provided. One way to improve the classification is by extending the feature set and selecting the best features out of this set. In this paper a set of noninformation-preserving features based on a multiresolution curve analysis which makes shape features explicit at multiple scales is proposed. An automatic procedure is designed to construct an optimal binary tree which is used to classify the unknown objects.