Image segmentation using an annealed Hopfield neural network

The authors 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. They are concerned not only with identifying the segmented regions, but also with finding a good approximation to the average gray level for each segment. A potential application is segmentation-based image coding. The approach is expected to find the global or nearly global solution fast using an annealing scheduling for the neural gains. A weak continuity constraints approach is used to define the appropriate optimization function. The simulation results for segmenting noisy images were very encouraging. Smooth regions were accurately maintained and boundaries were detected correctly.<>