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.