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Object recognition of one D.O.F. tools by a backpropagation neural network

We consider the object recognition of industrial tools which have one degree of freedom. In the case of pliers, the shape varies as the jaw angle varies. Thus, a feature vector made from the boundary image also varies along with the jaw angle. But a pattern recognizer should have the ability of classifying objects without any regards to the angle variation. For a pattern recognizer we have utilized a backpropagation neural net. Feature vectors were made from Fourier descriptors of boundary images by truncating the high frequency components, and they were used as inputs to the neural net for training and recognition. In our experiments, backpropagation neural net outperforms the minimum distance rule which is widely used in the pattern recognition. The performance comparison also made under noisy environments.