Neural network for image Fourier transform classification

Neural network for image Fourier transform classification

Considers the performance of a neural-network (NN)-based visual control system with NNs of different types (multilayered perceptrons and Hamming nets). They discuss the possible compensation of disturbances arising in a coherent-optical processor by NN learning. Simulation shows that different NNs h...

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Bibliographische Detailangaben
Hauptverfasser: Levchenko, E.B., Myl'nikov, G.D., Timashev, A.N., Turygin, A.Y.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Fourier transforms
Image processing
Image recognition
Neural networks
Nonlinear optical devices
Nonlinear optics
Optical computing
Optical devices
Optical fiber networks
Optoelectronic devices
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Zusammenfassung:Considers the performance of a neural-network (NN)-based visual control system with NNs of different types (multilayered perceptrons and Hamming nets). They discuss the possible compensation of disturbances arising in a coherent-optical processor by NN learning. Simulation shows that different NNs have different behaviors for two types of input distorted data: the perceptron NN is more suitable for compensation of optical tract errors while the winner-takes-all NN performs better for noise damaged input patterns.< >
DOI:10.1109/RNNS.1992.268593