Fast neural networks for sub-matrix (object/face) detection
Hazem Mokhtar El-Bakry, Herbert Stoyan · 2004
In recent years, fast neural networks for sub-matrix (object/face) detection have been introduced based on cross correlation in frequency domain between the input image and the weights of neural networks. In H. M. El-Bakry (2003), it has been proved that for those fast neural networks, either the weights of neural networks or the input image must be symmetric. In case of converting the input image into a symmetric one, those fast neural networks become slower than conventional neural networks. In this paper, a new form of symmetry for he input image to fast the operation of neural nets is presented. Simulation results using Matlab confirm the theoretical computations.