Establishing Hyper-Renormalization for Geometric Estimation from Images
| Project/Area Number |
24650086
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Okayama University |
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Principal Investigator |
KANATANI Kenichi 岡山大学, 大学院・自然科学研究科, 教授 (60125838)
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| Co-Investigator(Renkei-kenkyūsha) |
SUGAYA Yasuyuki 岡山大学, 大学病院, 准教授 (00335580)
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| Project Period (FY) |
2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2012: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 超精度くりこみ法 / 拡張 FNS 法 / 超精度補正 / 幾何学的推定 / 最尤推定 / 3次元形状復元 / 楕円当てはめ / カメラ校正 / 拡張FNS法 |
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| Research Abstract |
While maximum likelihood has been used for high accuracy geometric estimation from image data, such as fitting lines and ellipses and computing the relationships between corresponding points over multiple images, we have derived a higher accuracy method, called “hyper-renormalization” and applied it to various practical problems. At the same time, we have extend the “hyper accurate correction” for correcting the maximum likelihood solution and confirmed that the same degree of accuracy can be reached. We have also introduced a new formulation of the extended FNS method and applied it to the analysis of the GPS land deformation data of the Great East Japan Earthquake.
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Report
(2 results)
Research Products
(27 results)
