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- Watanabe Toshihiko
- Osaka Electro-Communication University
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- Saito Yuichi
- Osaka Electro-Communication University
Bibliographic Information
- Other Title
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- 強化学習に基づくファジィRANSACアルゴリズム
- キョウカ ガクシュウ ニ モトズク ファジィ RANSAC アルゴリズム
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Abstract
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 is 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 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 robustness in terms of modeling performance.
Journal
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- Proceedings of the Fuzzy System Symposium
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Proceedings of the Fuzzy System Symposium 28 (0), 991-993, 2012
Japan Society for Fuzzy Theory and Intelligent Informatics
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Details 詳細情報について
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- CRID
- 1390001205673312128
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- NII Article ID
- 130005456316
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- NII Book ID
- AA12165648
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- ISSN
- 18820212
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- NDL BIB ID
- 024000681
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- Data Source
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- JaLC
- NDL
- CiNii Articles
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- Abstract License Flag
- Disallowed