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Volumn 2016-December, Issue , 2016, Pages 3899-3908

Video Segmentation via Object Flow

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; ITERATIVE METHODS; MOTION ESTIMATION; OPTICAL FLOWS; PATTERN RECOGNITION;

EID: 84986255650     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.423     Document Type: Conference Paper
Times cited : (419)

References (47)
  • 3
    • 0030259484 scopus 로고    scopus 로고
    • Estimating optical flow in segmented images using variable-order parametric models with local deformations
    • Oct.
    • M. J. Black and A. Jepson. Estimating optical flow in segmented images using variable-order parametric models with local deformations. PAMI, 18(10):972-986, Oct. 1996.
    • (1996) PAMI , vol.18 , Issue.10 , pp. 972-986
    • Black, M.J.1    Jepson, A.2
  • 4
    • 4344598245 scopus 로고    scopus 로고
    • An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision
    • Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. PAMI, pages 1124-1137, 2004.
    • (2004) PAMI , pp. 1124-1137
    • Boykov, Y.1    Kolmogorov, V.2
  • 5
    • 77953223104 scopus 로고    scopus 로고
    • Video object segmentation by tracking regions
    • W. Brendel and S. Todorovic. Video object segmentation by tracking regions. In ICCV, 2009.
    • (2009) ICCV
    • Brendel, W.1    Todorovic, S.2
  • 6
    • 79551562584 scopus 로고    scopus 로고
    • Large displacement optical flow: Descriptor matching in variational motion estimation
    • T. Brox and J. Malik. Large displacement optical flow: descriptor matching in variational motion estimation. PAMI, 33(3):500-13, 2011.
    • (2011) PAMI , vol.33 , Issue.3 , pp. 500-513
    • Brox, T.1    Malik, J.2
  • 7
    • 84898769650 scopus 로고    scopus 로고
    • Topology-constrained layered tracking with latent flow
    • J. Chang and J. W. Fisher. Topology-constrained layered tracking with latent flow. In ICCV, 2013.
    • (2013) ICCV
    • Chang, J.1    Fisher, J.W.2
  • 8
    • 84887381501 scopus 로고    scopus 로고
    • A video representation using temporal superpixels
    • J. Chang, D. Wei, and J. W. Fisher. A video representation using temporal superpixels. In CVPR, 2013.
    • (2013) CVPR
    • Chang, J.1    Wei, D.2    Fisher, J.W.3
  • 9
    • 84887336889 scopus 로고    scopus 로고
    • Large displacement optical flow from nearest neighbor fields
    • Z. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow from nearest neighbor fields. In CVPR, 2013.
    • (2013) CVPR
    • Chen, Z.1    Jin, H.2    Lin, Z.3    Cohen, S.4    Wu, Y.5
  • 10
    • 77953216239 scopus 로고    scopus 로고
    • Adaptive fragments-based tracking of non-rigid objects using level sets
    • P. Chockalingam, N. Pradeep, and S. Birchfield. Adaptive fragments-based tracking of non-rigid objects using level sets. In ICCV, 2009.
    • (2009) ICCV
    • Chockalingam, P.1    Pradeep, N.2    Birchfield, S.3
  • 11
    • 84898817849 scopus 로고    scopus 로고
    • A unified video segmentation benchmark: Annotation, metrics and analysis
    • F. Galasso, N. Nagaraja, T. Cardenas, T. Brox, and B. Schiele. A unified video segmentation benchmark: Annotation, metrics and analysis. In ICCV, 2013.
    • (2013) ICCV
    • Galasso, F.1    Nagaraja, N.2    Cardenas, T.3    Brox, T.4    Schiele, B.5
  • 12
    • 84856645114 scopus 로고    scopus 로고
    • Hough-based tracking of non-rigid objects
    • M. Godec, P. M. Roth, and H. Bischof. Hough-based tracking of non-rigid objects. In ICCV, 2011.
    • (2011) ICCV
    • Godec, M.1    Roth, P.M.2    Bischof, H.3
  • 13
    • 77955986879 scopus 로고    scopus 로고
    • Efficient hierarchical graph-based video segmentation
    • M. Grundmann, V. Kwatra, M. Han, and I. Essa. Efficient hierarchical graph-based video segmentation. In CVPR, 2010.
    • (2010) CVPR
    • Grundmann, M.1    Kwatra, V.2    Han, M.3    Essa, I.4
  • 14
    • 84959205272 scopus 로고    scopus 로고
    • Supervoxel-consistent foreground propagation in video
    • S. D. Jain and K. Grauman. Supervoxel-consistent foreground propagation in video. In ECCV, 2014.
    • (2014) ECCV
    • Jain, S.D.1    Grauman, K.2
  • 15
    • 0027882076 scopus 로고
    • Mixture models for optical flow computation
    • A. Jepson and M. J. Black. Mixture models for optical flow computation. In CVPR, 1993.
    • (1993) CVPR
    • Jepson, A.1    Black, M.J.2
  • 16
    • 0035686705 scopus 로고    scopus 로고
    • Learning flexible sprites in video layers
    • N. Jojic and B. Frey. Learning flexible sprites in video layers. In CVPR, 2001.
    • (2001) CVPR
    • Jojic, N.1    Frey, B.2
  • 18
    • 84863045576 scopus 로고    scopus 로고
    • Key-segments for video object segmentation
    • Y. J. Lee, J. Kim, and K. Grauman. Key-segments for video object segmentation. In ICCV, 2011.
    • (2011) ICCV
    • Lee, Y.J.1    Kim, J.2    Grauman, K.3
  • 19
    • 84898791742 scopus 로고    scopus 로고
    • Video segmentation by tracking many figure-ground segments
    • F. Li, T. Kim, A. Humayun, D. Tsai, and J. M. Rehg. Video segmentation by tracking many figure-ground segments. In ICCV, 2013.
    • (2013) ICCV
    • Li, F.1    Kim, T.2    Humayun, A.3    Tsai, D.4    Rehg, J.M.5
  • 20
    • 84973869904 scopus 로고    scopus 로고
    • Hierarchical convolutional features for visual tracking
    • C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang. Hierarchical convolutional features for visual tracking. In ICCV, 2015.
    • (2015) ICCV
    • Ma, C.1    Huang, J.-B.2    Yang, X.3    Yang, M.-H.4
  • 21
    • 84866714644 scopus 로고    scopus 로고
    • Maximum weight cliques with mutex constraints for video object segmentation
    • T. Ma and L. J. Latecki. Maximum weight cliques with mutex constraints for video object segmentation. In CVPR, 2012.
    • (2012) CVPR
    • Ma, T.1    Latecki, L.J.2
  • 22
    • 84973866762 scopus 로고    scopus 로고
    • Video segmentation with just a few strokes
    • N. S. Nagaraja, F. Schmidt, and T. Brox. Video segmentation with just a few strokes. In ICCV, 2015.
    • (2015) ICCV
    • Nagaraja, N.S.1    Schmidt, F.2    Brox, T.3
  • 23
    • 84901822916 scopus 로고    scopus 로고
    • Segmentation of moving objects by long term video analysis
    • P. Ochs, J. Malik, and T. Brox. Segmentation of moving objects by long term video analysis. PAMI, 36(6):1187-1200, 2014.
    • (2014) PAMI , vol.36 , Issue.6 , pp. 1187-1200
    • Ochs, P.1    Malik, J.2    Brox, T.3
  • 24
    • 84898831797 scopus 로고    scopus 로고
    • Fast object segmentation in unconstrained video
    • A. Papazoglou and V. Ferrari. Fast object segmentation in unconstrained video. In ICCV, 2013.
    • (2013) ICCV
    • Papazoglou, A.1    Ferrari, V.2
  • 26
    • 34948907894 scopus 로고    scopus 로고
    • Tracking as repeated figure/ground segmentation
    • X. Ren and J. Malik. Tracking as repeated figure/ground segmentation. In CVPR, 2007.
    • (2007) CVPR
    • Ren, X.1    Malik, J.2
  • 27
    • 84877632511 scopus 로고    scopus 로고
    • Grabcut: Interactive foreground extraction using iterated graph cuts
    • C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. In SIGGRAPH, 2004.
    • (2004) SIGGRAPH
    • Rother, C.1    Kolmogorov, V.2    Blake, A.3
  • 29
    • 85009903791 scopus 로고    scopus 로고
    • Local layering for joint motion estimation and occlusion detection
    • D. Sun, C. Liu, and H. Pfister. Local layering for joint motion estimation and occlusion detection. In CVPR, 2013.
    • (2013) CVPR
    • Sun, D.1    Liu, C.2    Pfister, H.3
  • 30
    • 84894901673 scopus 로고    scopus 로고
    • A quantitative analysis of current practices in optical flow estimation and the principles behind them
    • D. Sun, S. Roth, and M. J. Black. A quantitative analysis of current practices in optical flow estimation and the principles behind them. IJCV, 106(2):115-137, 2014.
    • (2014) IJCV , vol.106 , Issue.2 , pp. 115-137
    • Sun, D.1    Roth, S.2    Black, M.J.3
  • 31
    • 84866657285 scopus 로고    scopus 로고
    • Layered segmentation and optical flow estimation over time
    • D. Sun, E. B. Sudderth, and M. J. Black. Layered segmentation and optical flow estimation over time. In CVPR, 2012.
    • (2012) CVPR
    • Sun, D.1    Sudderth, E.B.2    Black, M.J.3
  • 32
    • 84887353574 scopus 로고    scopus 로고
    • A fully-connected layered model of foreground and background flow
    • D. Sun, J. Wulff, E. B. Sudderth, H. Pfister, and M. J. Black. A fully-connected layered model of foreground and background flow. In CVPR, 2013.
    • (2013) CVPR
    • Sun, D.1    Wulff, J.2    Sudderth, E.B.3    Pfister, H.4    Black, M.J.5
  • 33
    • 79952783476 scopus 로고    scopus 로고
    • Dense point trajectories by GPU-accelerated large displacement optical flow
    • N. Sundaram, T. Brox, and K. Keutzer. Dense point trajectories by gpu-accelerated large displacement optical flow. In ECCV, 2010.
    • (2010) ECCV
    • Sundaram, N.1    Brox, T.2    Keutzer, K.3
  • 34
    • 0035279074 scopus 로고    scopus 로고
    • An integrated Bayesian approach to layer extraction from image sequences
    • Mar.
    • P. H. S. Torr, R. Szeliski, and P. Anandan. An integrated Bayesian approach to layer extraction from image sequences. PAMI, 23(3):297-303, Mar. 2001.
    • (2001) PAMI , vol.23 , Issue.3 , pp. 297-303
    • Torr, P.H.S.1    Szeliski, R.2    Anandan, P.3
  • 35
    • 84898465039 scopus 로고    scopus 로고
    • Motion coherent tracking with multi-label mrf optimization
    • D. Tsai, M. Flagg, and J. M. Rehg. Motion coherent tracking with multi-label mrf optimization. In BMVC, 2010.
    • (2010) BMVC
    • Tsai, D.1    Flagg, M.2    Rehg, J.M.3
  • 36
    • 84898797894 scopus 로고    scopus 로고
    • Active frame selection for label propagation in videos
    • S. Vijayanarasimhan and K. Grauman. Active frame selection for label propagation in videos. In ECCV, 2012.
    • (2012) ECCV
    • Vijayanarasimhan, S.1    Grauman, K.2
  • 37
    • 0028498068 scopus 로고
    • Representing moving images with layers
    • J. Y. A. Wang and E. H. Adelson. Representing moving images with layers. TIP, 3:625-638, 1994.
    • (1994) TIP , vol.3 , pp. 625-638
    • Wang, J.Y.A.1    Adelson, E.H.2
  • 39
    • 84898830536 scopus 로고    scopus 로고
    • Deepflow: Large displacement optical flow with deep matching
    • P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. Deepflow: Large displacement optical flow with deep matching. In ICCV, 2013.
    • (2013) ICCV
    • Weinzaepfel, P.1    Revaud, J.2    Harchaoui, Z.3    Schmid, C.4
  • 40
    • 84959236471 scopus 로고    scopus 로고
    • Jots: Joint online tracking and segmentation
    • L.Wen, D. Du, Z. Lei, S. Z. Li, and M.-H. Yang. Jots: Joint online tracking and segmentation. In CVPR, 2015.
    • (2015) CVPR
    • Wen, L.1    Du, D.2    Lei, Z.3    Li, S.Z.4    Yang, M.-H.5
  • 41
    • 84959241392 scopus 로고    scopus 로고
    • Efficient sparse-to-dense optical flow estimation using a learned basis and layers
    • J.Wulff and M. J. Black. Efficient sparse-to-dense optical flow estimation using a learned basis and layers. In CVPR, 2015.
    • (2015) CVPR
    • Wulff, J.1    Black, M.J.2
  • 42
    • 84881103424 scopus 로고    scopus 로고
    • Streaming hierarchical video segmentation
    • C. Xu, C. Xiong, and J. J. Corso. Streaming hierarchical video segmentation. In ECCV, 2012.
    • (2012) ECCV
    • Xu, C.1    Xiong, C.2    Corso, J.J.3
  • 43
    • 76849107760 scopus 로고    scopus 로고
    • A segmentation based variational model for accurate optical flow estimation
    • L. Xu, J. Chen, and J. Jia. A segmentation based variational model for accurate optical flow estimation. In ECCV, 2008.
    • (2008) ECCV
    • Xu, L.1    Chen, J.2    Jia, J.3
  • 44
    • 77955995204 scopus 로고    scopus 로고
    • Motion detail preserving optical flow estimation
    • L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. In CVPR, 2010.
    • (2010) CVPR
    • Xu, L.1    Jia, J.2    Matsushita, Y.3
  • 45
    • 84887400612 scopus 로고    scopus 로고
    • Video object segmentation through spatially accurate and temporally dense extraction of primary object regions
    • D. Zhang, O. Javed, and M. Shah. Video object segmentation through spatially accurate and temporally dense extraction of primary object regions. In CVPR, 2013.
    • (2013) CVPR
    • Zhang, D.1    Javed, O.2    Shah, M.3
  • 46
    • 85009850590 scopus 로고    scopus 로고
    • A background layer model for object tracking through occlusion
    • Y. Zhou and H. Tao. A background layer model for object tracking through occlusion. In CVPR, 2003.
    • (2003) CVPR
    • Zhou, Y.1    Tao, H.2
  • 47
    • 33745923511 scopus 로고    scopus 로고
    • Consistent segmentation for optical flow estimation
    • C. L. Zitnick, N. Jojic, and S. B. Kang. Consistent segmentation for optical flow estimation. In ICCV, 2005.
    • (2005) ICCV
    • Zitnick, C.L.1    Jojic, N.2    Kang, S.B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.