A Least Median of Squares Method Based on Fuzzy Reinforcement Learning for Modeling of Computer Vision Applications

The computer vision approach involves a lot of modeling problems in preventing noise caused by sensing units such as cameras and projectors. In order to improve computer vision modeling performance, a robust modeling technique must be developed for essential models in the system. The RANSAC and least median of squares (LMedS) algorithms have been widely applied in such issues. However, the performance deteriorates as the noise ratio increases and the modeling time for algorithms tends to increase in actual applications. In this study, we propose a new LMedS method based on fuzzy reinforcement learning concept for modeling of computer vision applications. The performance of the algorithm is evaluated by modeling synthetic data and camera homography experiments. Their results found the method to be effective in improving calculation time, model optimality, and robustness in modeling performance.