Fast sub-image segmentation using parallel neural processors and image decomposition

Abstract

In this paper, an approach to reduce the computation steps required by fast neural networks for the searching process is presented. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size sub-images and then each one is tested separately using a fast neural network. The operation of fast neural networks based on applying cross correlation in the frequency domain between the input image and the weights of the hidden neurons. Compared to conventional and fast neural networks, experimental results show that a speed up ratio is achieved when applying this technique to locate human faces automatically in cluttered scenes. Furthermore, faster face detection is obtained by using parallel processing techniques to test the resulting sub-images at the same time using the same number of fast neural networks. In contrast to using only fast neural networks, the speed up ratio is increased with the size of the input image when using fast neural networks and image decomposition. This is our new achievement over our previous publications ^1,2,7,9. Moreover, simulation results are increased more than those presented in our previous publications.