Disparity Diffusion/Absorption-Based Stereo Matching Using Cellular Evolutionary Neural Network with Initial Disparity Optimization

We have been proposing a disparity propagation-based stereo matching algorithm using cellular evolutionary neural network (CEN). Although our previous work demonstrated our algorithm is able to obtain decent accuracy for various scenes with low computational cost, its accuracy is limited by the simple initial disparity calculation and lack of proper propagation. In this paper, therefore, we propose an initial disparity optimization and disparity diffusion/absorption-based approach. The first feature mainly calculates initial disparities by an evolutionary-optimized matching cost function. The second feature then diffuse/absorb them according to the reliability of the initial disparity by utilizing state transition of CEN. Experimental results show that our new algorithm exceeds the common methods and our previous one with low computational cost, indicating the key features boost accuracy, especially for texture less regions, without being computationally expensive.