Complex nonlinear exponential autoregressive model for shape recognition using neural networks

A complex nonlinear exponential autoregressive (CNEAR) process which models the boundary coordinate sequence for invariant feature extraction to recognize arbitrary shapes on a plane is presented. All the CNEAR coefficients can be synchronically calculated by using a neural network which is simple in structure and, therefore, easy in implementation. The coefficients are adopted to constitute the feature set which are proven to be invariant to the transformation of a boundary such as translation, rotation, scale and choice of the starting point in tracing the boundary. Afterwards, the feature set is used as the input to a complex multilayer perceptron (C-MLP) network for learning and classification. Experimental results show that complicated shapes can be recognized in high accuracy, even in the low-order model. It is also seen that the classification method has a good degree of fault tolerance when noise is present.