2018 Fiscal Year Final Research Report
Fast and memory-less multi modality image matching based on higher order graph matching
| Project/Area Number |
16K16087
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Perceptual information processing
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| Research Institution | Tottori University |
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Principal Investigator |
OYAMADA Yuji 鳥取大学, 工学研究科, 助教 (30708615)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | Image Matching / Deep Learning |
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| Outline of Final Research Achievements |
I have changed the original research plan and done the following works. Task 1 is a deep learning solution for a rigid motion estimation from 2 images. Task 2 is an application based on the previous outcome such as transparent markers and inpainting.I have done 2 presentation in international conference and received 1 Honorable Mentions for a poster presentation.
I have conducting some experiments to compare the method and state-of-the-arts on multi modality image matching.
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| Free Research Field |
Computer Vision
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| Academic Significance and Societal Importance of the Research Achievements |
画像のマッチングは複数枚の画像を比較し画像から有用な情報を抽出するために必要な前処理である．深層学習による画像のマッチングにより，既存の画像解析システム・アプリの適用先が広がると考えられる．
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