| Project/Area Number |
20700164
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Yamaguchi University |
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Principal Investigator |
HIRABAYASHI Akira Yamaguchi University, 大学院・医学系研究科, 准教授 (50272688)
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| Co-Investigator(Renkei-kenkyūsha) |
PIER-LUIGI Dragotti ロンドンインペリアル大学
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| Project Period (FY) |
2008 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2009: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2008: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | スパース性 / 標本化 / マルチチャンネル標本化 / MAP推定 / l1ノルム最小化 / 単位長自由度有限信号 / 画像特徴量抽出 / サンプリング / 信号再構成 / 超解像 / 特徴量 / Spline / ハフ変換 / 標本化(サンプリング) / インターレース標本化 / 事後確率最大(MAP)推定 / 11ノルム最小化原理 / 非線形性 |
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| Research Abstract |
We studied multi-channel sampling for sparse signals and image feature extraction using the sampling scheme. The former was mainly tackled in the first year, and we proposed two algorithms ; one is the so-called MAP estimation algorithm with a prior distribution of signals, and the other is the l1-norm minimization algorithm. In the second year, we devised an exact line-edge extraction algorithm that can be applied for super-resolution. We showed by computer simulations that the proposed method is approximately 10 dB better against noise than the conventional methods.
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