| Project/Area Number |
23500212
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Ritsumeikan University (2013)
Yamaguchi University (2011-2012) |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
YANAGIHARA Hiroshi 山口大学, 理工学研究科, 准教授 (30200538)
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| Research Collaborator |
DRAGOTTI Pier-luigi ロンドンインペリアル大学, 准教授
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2011: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | エッジ検出 / ハフ変換 / 点広がり関数 / 標本化理論 / E-Spline / 不確定率有限信号 / スパース性 / サンプリング / スプライン / 粒子群最適化 / 圧縮センシング / クロネッカー積 / 近似的メッセージ交換アルゴリズム / 画像エッジ抽出 / 画像位置合わせ / 写真計測 / 凸最適化 / 画像解析 / 直線エッジ抽出 / 標本化 / 最適化 |
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| Research Abstract |
We proposed step line edge extraction method using E-spline functions as sampling model. A straight line-edge can be described by three parameters of orientation, offset, and amplitude. The standard method to extract these parameters is Hough transform, which has a limitation in principle to achieve sub-pixel accuracy. Such a limitation can be resolved by introducing a pixel acquisition model. Conventional approaches along this context used analytic solutions for the three parameters, which are sensitive to noise in pixels. To solve the problem, we proposed an optimization approach. We first define a criterion that evaluates a distance between exponential moments obtained from noisy pixel values and its theoretical closed form. Its optimization is conducted by a simple coarse-to-fine search. Computer simulations showed that not only noise resilience is much improved by the proposed method but also it extracts line-edges from real images more accurately than the Hough transform.
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