Doshisha University Masahiro Okuda|同志社大学 知的機構研究室 奥田正浩

Research 研究内容

Neural Networksの潜在バイアス分析

Deep Learningを用いたドメイン分布の獲得,潜在バイアスコントロール,非定型データの潜在空間表現などの研究


応用例:経済指標の予測,機器の寿命予測,購買予測 など



  • 医療画像を用いた生存期間予測
  • 3次元航空画像のセグメンテーション

Medical Image Restoration by Deep Neural Network深層学習を用いた医歯用画像の復元~北九州医療ICT基盤の構築~

総務省 戦略的情報通信研究開発推進事業(SCOPE:研究代表者 奥田正浩)
環境技術研究所 重点研究推進支援プロジェクト(研究代表者 奥田正浩)


We conduct research on medical and dental image analysis using signal processing and artificial intelligence jointly with the University of Occupational and Environmental Health and Kyushu Dental University.

High Dynamic Range Imaging高ダイナミックレンジ(HDR)画像処理

"Weight Optimization for Multiple Image Integration and Its Applications,"
Ryo Matsuoka, Tomohiro Yamauchi, Tatsuya Baba, Masahiro Okuda,
IEICE Transactions on Information and Systems, Vol.E99-D,No.1,pp.228-235,Jan. 2016,

We propose an image restoration technique that uses multiple image integration. The detail of the dark area when acquiring a dark scene is often deteriorated by sensor noise. Simple image integration inherently has the capability of reducing random noises, but it is especially insufficient in scenes that have a dark area. We introduce a novel image integration technique that optimizes the weights for the integration.


3D imaging3次元画像処理

3D mesh parameterization is a method which converts the complicated 3D mesh into the flat and non-overlapped 2D mesh, and is used for "texture-mapping" to make the correspondence between a texture-image and a 3D mesh in 2D space, and "remeshing" to convert irregular meshes into more manageable meshes. In this paper, we propose a 3D mesh parameterization method which is able to express more detailed shape of the 3D model. However, this one-sided emphasis on "remeshing" incurs texture-distortions in practice. So, we also propose a texture-mapping method which uses a transform-map of texture-coordinates to keep "texture-mapping" qualities.