<title>Image segmentation using an annealed Hopfield neural network</title>

Abstract

Good image segmentation can be achieved by finding the optimum solution to an appropriate energy function. A Hopfield neural network has been shown to solve complex optimization problems fast, but it only guarantees convergence to a local minimum of the optimization function. Alternatively, mean field annealing has been shown to reach the global or the nearly global optimum solution when solving optimization problems. Furthermore, it has been shown that there is a relationship between a Hopfield neural network and mean field annealing. In this paper, we combine the advantages of the Hopfield neural network and the mean field annealing algorithm and propose using an annealed Hopfield neural network to achieve good image segmentation fast. Here, we are concerned not only with identifying the segmented regions, but also finding a good approximation to the average gray level for each segment. A potential application is segmentation-based image coding. This approach is expected to find the global or nearly global solution fast using an annealing schedule for the neural gains. A weak continuity constraints approach is used to define the appropriate optimization function. The simulation results for segmenting noisy images are very encouraging. Smooth regions were accurately maintained and boundaries were detected correctly.