Edge detection using hopfield neural network

Hopfield neural network has been widely applied in many areas. Its highly interconnected structure of neurons is not only very effective in computational complexity but also very fault-Tolerant. Such a neural network has been used as analog computational networks for solving optimization problems. The low-level image processing of edge detection can also be regarded as an optimization problem. This paper presents an edge detection algorithm using Hopfield neural network. This algorithm brings up a new concept which is different from those conventional differentiation operators, such as Sobel and Laplacian. In this algorithm, an image is considered a dynamic system which is completely depicted by an energy function. In other words, an image is described by a set of interconnected neurons. Every pixel in the image is represented by a neuron which is connected to all other neurons but not to itself. The weight of connection between two neurons is described as being a function of contrast of gray-level values and the distance between pixels. The initial state of each neuron represents the normalized gray-level value of the corresponding pixel in the original image. As a result of Hopfield network analysis, output of neurons is modified till the convergence. Even though the outputs are analog, they are close to zero in all regions except edges where the corresponding neurons have near 1 .0 output values. A robust threshold on the output level of the converged network can be easily set up at 0.5 level to extract edges. The experimental results are presented to show the effectiveness and capability of this algorithm.