By Hideyuki Tamura, Takashi Matsuyama, Naokazu Yokoya, Ryosuke Ichikari, Shohei Nobuhara (auth.), Reinhard Koch, Fay Huang (eds.)
The two-volume set LNCS 6468-6469 includes the conscientiously chosen and reviewed papers awarded on the 8 workshops that have been held along side the tenth Asian convention on laptop imaginative and prescient, in Queenstown, New Zealand, in November 2010.
From a complete of 167 submissions to all workshops, 89 papers have been chosen for e-book. The contributions are grouped jointly in keeping with the most workshops issues, that have been: computational images and aesthetics; desktop imaginative and prescient in automobile expertise: from Earth to Mars; digital cultural historical past; subspace established tools; video occasion categorization, tagging and retrieval; visible surveillance; program of desktop imaginative and prescient for combined and augmented reality.
Read or Download Computer Vision – ACCV 2010 Workshops: ACCV 2010 International Workshops, Queenstown, New Zealand, November 8-9, 2010, Revised Selected Papers, Part II PDF
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Additional info for Computer Vision – ACCV 2010 Workshops: ACCV 2010 International Workshops, Queenstown, New Zealand, November 8-9, 2010, Revised Selected Papers, Part II
Complex human motion estimation using visibility. In: IEEE Int. Conf. Series on Automatic Face and Gesture Recognition (2008) 7. : Parallel tracking and ma pping for small AR workspaces. In: Proc. ISMAR, pp. 225–234 (2007) 8. : Real-time geometric registration using feature landmark database for augmented reality applications. In: Proc. SPIE (2009) 723804–723804–9 9. : Extrinsic camera parameter recovery from multiple image sequences captured by an omni-directional multi-camera system. In: Proc.
Yiming et al. the map and the keypoints extracted from input image in a local way. In most SLAM varieties, a motion model is used to predict the current camera pose, and afterwards the optimization in LL scheme will converge near the prediction. Consequently, the ﬁnal result highly depends on the initial state (the predicted camera pose). Once the prediction is not reliable, the local optimization tends to converge to an incorrect state, and subsequently leads to the failure of tracking. g. sudden camera move, error accumulation.
461 Computer Vision Technology Applied to MR-Based Pre-visualization in Filmmaking Hideyuki Tamura1 , Takashi Matsuyama2 , Naokazu Yokoya3, Ryosuke Ichikari1 , Shohei Nobuhara2 , and Tomokazu Sato3 1 3 College of Information Scienece and Engineering, Ritsumeikan University 2 Graduate School of Informatics, Kyoto University Graduate School of Information Science, Nara Institute of Science and Technology Abstract. In this talk, we introduce the outline of gThe MR-PreViz Projecth performed in Japan.
Computer Vision – ACCV 2010 Workshops: ACCV 2010 International Workshops, Queenstown, New Zealand, November 8-9, 2010, Revised Selected Papers, Part II by Hideyuki Tamura, Takashi Matsuyama, Naokazu Yokoya, Ryosuke Ichikari, Shohei Nobuhara (auth.), Reinhard Koch, Fay Huang (eds.)