Initial Learning Stage Improvement for Fuzzy LMedS Algorithm Based on Weighted Estimation Model

Abstract

The computer vision involves many modeling problems with preventing noise caused by sensing units such as cameras. In order to improve computer vision system performance, a robust modeling technique must be developed for essential models in the system. The RANSAC and least median of squares (LMedS) algorithm have been widely applied for such issues. However, the performance deteriorates as the noise ratio increases and the modeling time for algorithms tends to increase in industrial applications. As an effective technique, we proposed a new fuzzy LMedS method based on reinforcement learning concept for robust modeling. In this study, we proposed a new technique for the fuzzy LMedS in order to improve learning performance in initial learning stage based on weighted estimation model. Through experiments of camera homography modeling, the performance of the technique was evaluated. Their results found the proposed technique to be promising for improving modeling performance in the initial learning stage.