| Project/Area Number |
16K00253
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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 |
Perceptual information processing
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| Research Institution | Chubu University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
上瀧 剛 熊本大学, 大学院先端科学研究部(工), 准教授 (20582935)
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| Research Collaborator |
AMBAI Mitsuru
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | キーポイントマッチング / 特徴記述 / 多視点記述子 / テンソル分解 / 視点合成フィルタ / 因子分解による低ランク近似 / 画像局所特徴量 / 下界計算 / コンピュータビジョン / 画像認識 |
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| Outline of Final Research Achievements |
Conventional multi-viewpoint descriptors, such as Affine SIFT (ASIFT), require much online affine-warping of a patch image to precisely match images that have viewpoint changes. Therefore, we propose affine invariant descriptor without conventional heavy online affine-warping. To this end, the proposed descriptor represents traditional local descriptors as the inner product between “feature-description filters” and a local patch image. By using feature-description filters, we can conduct affine-warping efficiently using pre-computed filter sets. In addition, affine-warped filters can be compactly represented using a factorization method, and the multi-viewpoint local features can be generated for arbitrary continuous affine parameters. Experimental results indicate that the proposed descriptor describes multi-viewpoint features more efficiently than conventional affine invariant descriptors while maintaining the keypoint matching performance.
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| Academic Significance and Societal Importance of the Research Achievements |
撮影位置を不連続にして撮影した画像には，視点変化によって生じる変化が含まれる．このような画像をつなぎ合わせるには，視点変化に影響を受けない特徴量記述が必要となる．本研究成果はこの問題を解決するアプローチであり，従来法より大幅な高速化を実現したことで，携帯型デバイスにおいての実行が可能となる．これにより，画像認識技術を用いた様々なアプリケーションの開発に貢献できる．
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