Robust estimation for camera homography by fuzzy RANSAC algorithm with reinforcement learning

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 functio...

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Bibliographic Details
Published in2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS) pp. 712 - 717
Main Authors Watanabe, Toshihiko, Kamai, Takeshi, Ishimaru, Tomoki
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2014
Subjects
Cameras
Computational modeling
Computer vision
Estimation
fuzzy set
Learning (artificial intelligence)
LMedS
Noise
RANSAC
reinforcement learning
robust estimation
Robustness
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Summary: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 and LMedS algorithm have been widely applied for such issues. However, the performance is deteriorated when the ratio of noises increases. Moreover the computational time for the algorithms tends to increase for actual applications. In this study, a new fuzzy RANSAC algorithm based on the reinforcement learning concept is proposed for homography estimation. The performance of the algorithm is evaluated through experiments of camera homography. From the results, the method is found to be effective to improve calculation time, optimality of the model, and robustness in terms of modeling performance.
DOI:10.1109/SCIS-ISIS.2014.7044890