Extensions of Hopfield Neural Networks for Solving of Stereo-Matching Problem

Paper considers three Hopfield based architectures in the stereo matching problem solving. Together with classical analogue Hopfield structure two novel architectures are examined: Hybrid-Maximum Neural Network and Self Correcting Neural Network. Energy functions that are crucial for the network performance and working algorithm are also presented. All considered structures are tested to compare their performance features. Two of them are particularly important: accuracy and computational time. For the experiment real and simulated stereo images are used. Obtained results lead to the conclusion about feasibility of considered architectures in the stereo matching problem solving for real time applications.