Representing and classifying 2D shapes of real-world objects using neural networks

A framework is presented which uses a polar representation of a segmented object for shape classification. This method produces a position, rotation and scale invariant representation of the shape. An efficient method for extracting multiple contours from the polar representation is used to handle the problem of many-to-one mappings in the radial and angular parameters. The contours are used to find interesting vertices of the shape. The shape information is mapped to spatial regions on a polar grid and fed into a multi-layer perceptron for classification. The framework is tested on manually segmented images of people's hands and on side views of automobiles. The results show that the network can achieve approximately 100% generalization on test data even though the network is under trained.