Feature Extraction And Pattern Classification In Space - Spatial Frequency Domain

A novel feature extraction method, useful for 2-D shape description, is proposed. It is based on an optimal representation of a 1-D signal in space - spatial frequency domain, the Wigner distribution. For shape clasification, one of the many 1-D representations of the 2-D contours is employed. Boundary features, or shape descriptors, are obtained using sigular value decomposition of the Wigner distribution (WD). Properties of WD singular values are presented and shown to encode certain shape features such as the space-bandwidth product, the shape complexity in terms of number of components and their spacing, and the spatial frequency vs. the space dependence. The singular values of the boundary Wigner distri bution possess all the properties required of good shape descriptors. To illustrate the effectiveness of these descriptors in shape classification, a number of examples are presented. The proposed method is useful for robust classification of any 1-D patterns.