A neural-net computing algorithm for detecting edges in a gray scale image

The algorithm presented features simple connections between neurons and is highly effective in detecting edges in noisy gray-scale images. The algorithm consists of two steps: image filtering and edge detection. The image filtering portion detects the zero crossings of D_i²G (x, y) × l(x, y) where D_i ²G (x, y) represents the second derivative taken in the ith direction of a 2-9 Gaussian function. The output is then normalized and used to initialize the i th layer of a multiple-layer, Hopfield-type, neural network with simple localized connections. During edge detection, processing elements either excite (cooperate with) or inhibit (complete with) processing elements in other positions and layers. The competitive-cooperative process among the processing elements builds up edges and eliminates noise. The algorithm was simulated by using only four orientations: 0°, 45°, 90°, and 135°. Simulation results show the algorithm´s performance on images with varying levels of noise. The output of this algorithm could be suitable input for a pattern recognition network