基本信息
浏览量:47
职业迁徙
个人简介
I am interested in Computer Vision and Machine Learning. At present, I specifically focus my research on probabilistic object tracking and general object recognition.
Journal
Wei-Lwun Lu, Kenji Okuma, Jim Little : " Tracking and Recognizing Actions of Multiple Hockey Players using the Boosted Particle Filter ," Image and Vision Computing. March 2008
[PDF]
Papers
Kenji Okuma, David G. Lowe, James J. Little: " Self-learning for player localization in sports video ," arXiv.org: arXiv:1307.7198 [cs.CV] July 2013
[PDF] [arXiv]
Kenji Okuma, Eric Brochu, David G. Lowe, James J. Little: " An Adaptive Interface for Active Localization ," International Conference on Computer Vision Theory and Application, March 2011
[PDF] [Project page]
Kenji Okuma, Ali Taleghani, Nando De Freitas, James J. Little, David G. Lowe: " A Boosted Particle Filter: Multitarget Detection and Tracking ," the European Conference on Computer Vision(ECCV), May 2004
Best Paper prize in Cognitive Vision
[PDF] [Demo Software]
Kenji Okuma, Jim Little, and David G. Lowe: " Automatic rectification of long image sequences " the Asian Conference on Computer Vision(ACCV), Jeju Island, Korea, January 2004
[PDF]
Kenji Okuma, Jim Little, and David G. Lowe: " Automatic Acquisition of Motion Trajectories: Tracking Hockey Players ," Manuscript of the oral presentation for the Internet Imaging V, SPIE 2004
[PDF]
Ellen L. Walker and Kenji Okuma: " Automatic Extraction of Invariant Features for Object Recognition ," in Proceedings of the Annual Conference of the North American Fuzzy Information Processing Society [NAFIPS '00], Atlanta, GA, pp. 163-167, June 2000.
[PDF]
Thesis
Kenji Okuma: "Active Exploration of Training Data for Improved Object Detection ," Ph.D. Thesis, the University of British Columbia, Feb 2012.
[PDF]
Kenji Okuma: "Automatic Acquisition of Motion Trajectories: Tracking Hockey Players ," M.Sc. Thesis, the University of British Columbia, May 2003.
[PDF]
研究兴趣
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Active Exploration of Training Data for Improved Object Detection: For cheaper training, faster development time, and higher-performance of object detectors (2012)
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Image and Vision Computingno. 1-2 (2009): 189-205
SPIE ProceedingsInternet Imaging V (2003)
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