A Rotation Invariant Algorithm for Recognition
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Analyzing Wavelets Components to Perform Face Recognition
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
A new feature extractor invariant to intensity, rotation, and scaling of color images
Information Sciences: an International Journal
Recognizing one-DOF industrial tools using invariant moments
Mathematical and Computer Modelling: An International Journal
Automatic recognition of industrial tools using artificial intelligence approach
Expert Systems with Applications: An International Journal
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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