Neural networks for planar shape classification
Lalit Gupta, M.R. Sayeh · 2003
A neural network approach is presented for the classification of closed planar shapes. The neural net classifier developed is robust and invariant to translation, rotation, and scaling. The primary foci are the development of an effective representation for planar shapes and the selection of a suitable neural network structure. In particular, planar shapes are represented by an ordered sequence that represents the Euclidean distance between the centroid and all contour pixels of the shape. It is also shown that for this classification problem and the representation derived, the three-layer perceptron with backpropagation training is an appropriate neural network configuration.>