Object recognition of one-DOF tools by a back-propagation neural net

Considers the recognition of industrial tools which have one degree of freedom (DOF). In the case of pliers, the shape varies as the jaw angle varies, and a feature vector made from the boundary image varies with it. For a pattern classifier that is able to classify objects without regard to angle variation, we have utilized a backpropagation neural net. Feature vectors made from Fourier descriptors of boundary images by truncating the high-frequency components were used as inputs to the neural net for training and recognition. In our experiments, the backpropagation neural net outperforms both the minimum-mean-distance and the nearest-neighbor rules which are widely used in pattern recognition. Performances are also compared under noisy environments and for some untrained objects.