Robust Estimation of Camera Homography by Fuzzy RANSAC Algorithm with Reinforcement Learning
Toshihiko Watanabe*, Takeshi Kamai**, and Tomoki Ishimaru*
*Graduate School of Engineering, Osaka Electro-Communication University
18-8 Hatsu-cho, Neyagawa, Osaka 572-8530, Japan
1-1-32 Kamiminami, Hirano-ku, Osaka 547-0003, Japan
The computer vision approach involves many modeling problems in preventing noise caused by sensing units such as cameras and projectors. To improve computer vision modeling performance, a robust modeling technique must be developed for essential models in the system. The RANSAC and LMedS algorithms have been widely applied in such issues, but performance deteriorates as the noise ratio increases and the calculation time for algorithms tends to increase in actual applications. In this study, we propose a new fuzzy RANSAC algorithm for homography estimation based on the reinforcement learning concept. 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.
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