Research on inference of visual images from incomplete neural activity data in primary visual cortex

2011 Fiscal Year Final Research Report

• Project/Area Number: 21700263
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Single-year Grants
• Research Field: Sensitivity informatics/Soft computing
• Research Institution: Waseda University
• Principal Investigator: INOUE Masato 早稲田大学, 理工学術院, 准教授 (70376953)
• Project Period (FY): 2009 – 2011
• Keywords: 確率的情報処理

Research Abstract

In the research process, we found that super resolution technique, which infers a high resolution image from low resolution images, is more substantial and general technique of the inference of visual images from visual cortex data. Afterward, we researched the super resolution and succeeded to develop the best method in low noise level situation. The method can analytically and approximately determine the optimal estimator over the original image model of the compound Markov random field prior. Such problem had been an unsolved problem for a long time.

Research Products

• [Journal Article] Posterior Mean Super-resolution with a Causal Gaussian Markov Random Field Prior (2012)
  • Author(s): Takayuki Katsuki, Akira Torii, Masato Inoue
  • Journal Title: IEEE Transactions on Image Processing Volume: (in press)
  • Peer Reviewed
• [Journal Article] 混合モデルとしての複層Gauss-Markov確率場による画像の修復と領域分割 (2011)
  • Author(s): 勝木孝行, 井上真郷
  • Journal Title: 電子情報通信学会技術研究報告 Volume: Vol.111, No.275 Pages: 223-230
• [Journal Article] 複層Markov確率場と線形劣化変換に対するBayes超解像 (2010)
  • Author(s): 勝木孝行, 鳥居英, 井上真郷
  • Journal Title: 電子情報通信学会技術研究報告 Volume: Vol.110, No.83 Pages: 63-68
• [Presentation] Posterior mean super-resolution with a compound Gaussian Markov random field prior (2012)
  • Author(s): Takayuki Katsuki, Masato Inoue
  • Organizer: IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP2012)
  • Place of Presentation: Kyoto
  • Year and Date: 2012-03-28
• [Presentation] 混合モデルとしての複層Gauss-Markov確率場による画像の修復と領域分割 (2011)
  • Author(s): 勝木孝行, 井上真郷
  • Organizer: 電子情報通信学会情報論的学習理論と機械学習研究会(IBISML), IBISML2011-11
  • Place of Presentation: 奈良
  • Year and Date: 2011-11-10
• [Presentation] 複層Markov確率場と線形劣化変換に対するBayes超解像 (2010)
  • Author(s): 勝木孝行, 鳥居英, 井上真郷
  • Organizer: 電子情報通信学会ニューロコンピューティング研究会
  • Place of Presentation: 沖縄
  • Year and Date: 2010-06-18
• [Remarks]
  • URL: http://www.eb.waseda.ac.jp/m_inoue/publications/
• [Remarks] 2010年度に当研究室修士学生で,上記論文,発表の第一著者である勝木孝行が, IEEE Computational Intelligence Society Japan ChapterよりYoung Researcher Awardを受賞した.