Applications of artificial neural networks II: 2-5 April 1991, Orlando, Florida, Part 1
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application approach architecture Artificial Neural Networks attractors back-propagation binary boundaries cells classification configuration connectionist connections convergence correlation corresponding detection detector discriminant DLGM dynamics early vision elements equation error example feature extraction feature space feature vectors filter FLIR Gabor Gabor filters Gaussian graph Hebbian learning hidden layer hidden units Hopfield image processing implementation input layer interconnection iterations labeled LCTV learning algorithm line process linear mapping matrix method minimization Morlet wavelets multilayer perceptron multiple neighbor Neocognitron neuron nodes noise nonlinear object plane operation optical optical correlator optical flow optimization orientation output layer parallel parameters pattern recognition performance pixel problem processor prototype quantization represents rules samples segmentation sensor shown in Figure sigmoid sigmoid function signal simulation solution spatial spatial light modulators target techniques template testing training set transformation update values vector quantization vision integration vision modules wavelet weights