Object recognition using a neural network with optimal feature extraction

Abstract

In this paper, a neural network using an optimal linear feature extraction scheme is proposed to recognize two-dimensional objects in an industrial environment. This approach consists of two stages. First, the procedures of determining the coefficients of normalized rapid descriptor (NRD) of unknown 2-D objects from their boundary are described. To speed up the learning process of the neural network, an optimal linear feature extraction technique is used to extract the principal components of these NRD coefficients. Then, these reduced components are utilized to train a feedforward neural network for object recognition. We compare recognition performance, network sizes, and training time for networks trained with both reduced and unreduced data. The experimental results show that a significant reduction in training time can be achieved without a sacrifice in classifier accuracy.