A fuzzy RANSAC algorithm based on reinforcement learning concept

In the computer vision approach, there are many problems of modeling to prevent affections of noises by sensing units such as cameras and projectors. In order to improve the performance of the modeling in the computer vision, it is necessary to develop a robust modeling technique for various functions. The RANSAC algorithm has been widely applied for such issues. However, the performance is deteriorated when the ratio of noises increases. In this study, a new fuzzy RANSAC algorithm based on the reinforcement learning concept is proposed. The essential performance of the algorithm is evaluated through numerical experiments. From the results, the method is found to be promising to improve calculation time, optimality of the model, and robustness in terms of modeling performance.