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

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

時系列データ解析

多変量の時系列データから未来を予測したり，データの異常や歪みを検知します．
応用例：経済指標の予測，機器の寿命予測，購買予測 など

高次元データからの知識抽出

高次元データ（例えばMRIなどの医療画像，航空データ，3次元点群データ）の裏に潜む価値ある情報を抽出する研究
研究事例

• 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～

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.
信号処理，人工知能を用いた医科・歯科画像解析の研究を産業医科大学，九州歯科大学と共同で行う．

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

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.

高ダイナミックレンジ画像とは

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.