Spatio-Temporal Context for More Accurate Dense Point Trajectories Estimation
暂无分享,去创建一个
[1] Zhenhua Wang,et al. Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Kurt Keutzer,et al. Dense Point Trajectories by GPU-Accelerated Large Displacement Optical Flow , 2010, ECCV.
[3] Jintao Li,et al. Hierarchical spatio-temporal context modeling for action recognition , 2009, CVPR.
[4] Vibhav Vineet,et al. A tiered move-making algorithm for general pairwise MRFs , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[5] Edward H. Adelson,et al. Human-assisted motion annotation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[6] Gang Hua,et al. Context aware topic model for scene recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[7] Stefano Soatto,et al. SuperFloxels: A Mid-level Representation for Video Sequences , 2012, ECCV Workshops.
[8] Seth J. Teller,et al. Particle Video: Long-Range Motion Estimation Using Point Trajectories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[9] Jitendra Malik,et al. Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[10] Carlo Tomasi,et al. Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
[11] Zhuowen Tu,et al. MRF Labeling with a Graph-Shifts Algorithm , 2008, IWCIA.