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Volumn 2016-December, Issue , 2016, Pages 1619-1627

Unsupervised learning of edges

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; EDGE DETECTION; MOTION ESTIMATION;

EID: 84986249794     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.179     Document Type: Conference Paper
Times cited : (96)

References (47)
  • 2
    • 79953048649 scopus 로고    scopus 로고
    • Contour detection and hierarchical image segmentation
    • 1, 2, 5
    • P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour detection and hierarchical image segmentation. PAMI, 2011.
    • (2011) PAMI
    • Arbelaez, P.1    Maire, M.2    Fowlkes, C.3    Malik, J.4
  • 5
    • 84973888826 scopus 로고    scopus 로고
    • High-for-low and low-for-high: Efficient boundary detection from deep object feat. And its app. To high-level vision
    • 1, 2
    • G. Bertasius, J. Shi, and L. Torresani. High-for-low and low-for-high: Efficient boundary detection from deep object feat. And its app. To high-level vision. In ICCV, 2015.
    • (2015) ICCV
    • Bertasius, G.1    Shi, J.2    Torresani, L.3
  • 6
    • 79551562584 scopus 로고    scopus 로고
    • Large displacement optical flow: Descriptor matching in variational motion estimation
    • 3
    • T. Brox and J. Malik. Large displacement optical flow: descriptor matching in variational motion estimation. PAMI, 2011.
    • (2011) PAMI
    • Brox, T.1    Malik, J.2
  • 7
    • 84887338408 scopus 로고    scopus 로고
    • A naturalistic open source movie for optical flow evaluation
    • 6
    • D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In ECCV, 2012.
    • (2012) ECCV
    • Butler, D.J.1    Wulff, J.2    Stanley, G.B.3    Black, M.J.4
  • 8
    • 0022808786 scopus 로고
    • A computational approach to edge detection
    • 1, 2
    • J. Canny. A computational approach to edge detection. PAMI, 1986.
    • (1986) PAMI
    • Canny, J.1
  • 9
    • 84973916088 scopus 로고    scopus 로고
    • Unsupervised visual representation learning by context prediction
    • 2, 6
    • C. Doersch, A. Gupta, and A. A. Efros. Unsupervised visual representation learning by context prediction. In ICCV, 2015.
    • (2015) ICCV
    • Doersch, C.1    Gupta, A.2    Efros, A.A.3
  • 10
    • 33845580709 scopus 로고    scopus 로고
    • Supervised learning of edges and object boundaries
    • 1, 2
    • P. Dollár, Z. Tu, and S. Belongie. Supervised learning of edges and object boundaries. In CVPR, 2006.
    • (2006) CVPR
    • Dollár, P.1    Tu, Z.2    Belongie, S.3
  • 11
    • 84947781852 scopus 로고    scopus 로고
    • Fast edge detection using structured forests
    • 1, 2, 4, 8
    • P. Dollár and C. L. Zitnick. Fast edge detection using structured forests. PAMI, 2015.
    • (2015) PAMI
    • Dollár, P.1    Zitnick, C.L.2
  • 13
    • 36448974509 scopus 로고    scopus 로고
    • Groups of adjacent contour segments for object detection
    • 1
    • V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid. Groups of adjacent contour segments for object detection. PAMI, 2008.
    • (2008) PAMI
    • Ferrari, V.1    Fevrier, L.2    Jurie, F.3    Schmid, C.4
  • 14
    • 84898803533 scopus 로고
    • On the quantitative evaluation of edge detection schemes and their comparison with human performance
    • 1, 2
    • J. R. Fram and E. S. Deutsch. On the quantitative evaluation of edge detection schemes and their comparison with human performance. IEEE TOC, 1975.
    • (1975) IEEE TOC
    • Fram, J.R.1    Deutsch, E.S.2
  • 15
    • 0026221555 scopus 로고
    • The design and use of steerable filters
    • 1, 2
    • W. T. Freeman and E. H. Adelson. The design and use of steerable filters. PAMI, 1991.
    • (1991) PAMI
    • Freeman, W.T.1    Adelson, E.H.2
  • 17
    • 85029359197 scopus 로고    scopus 로고
    • Fast R-CNN
    • 6, 7
    • R. Girshick. Fast R-CNN. In ICCV, 2015.
    • (2015) ICCV
    • Girshick, R.1
  • 19
    • 84969584486 scopus 로고    scopus 로고
    • Batch normalization: Accelerating deep network training by reducing internal covariate shift
    • 5
    • S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015.
    • (2015) ICML
    • Ioffe, S.1    Szegedy, C.2
  • 20
    • 84959243006 scopus 로고    scopus 로고
    • Visual boundary prediction: A deep neural prediction network and quality dissection
    • 2
    • J. J. Kivinen, C. K. Williams, and N. Heess. Visual boundary prediction: A deep neural prediction network and quality dissection. In AISTATS, 2014.
    • (2014) AISTATS
    • Kivinen, J.J.1    Williams, C.K.2    Heess, N.3
  • 23
    • 84887354170 scopus 로고    scopus 로고
    • Sketch tokens: A learned mid-level representation for contour and object detection
    • 1, 2
    • J. Lim, C. L. Zitnick, and P. Dollár. Sketch tokens: A learned mid-level representation for contour and object detection. In CVPR, 2013.
    • (2013) CVPR
    • Lim, J.1    Zitnick, C.L.2    Dollár, P.3
  • 24
    • 0019647180 scopus 로고
    • An iterative image registration technique with an application to stereo vision
    • 2
    • B. D. Lucas, T. Kanade, et al. An iterative image registration technique with an application to stereo vision. In IJCAI, 1981.
    • (1981) IJCAI
    • Lucas, B.D.1    Kanade, T.2
  • 25
    • 3042525106 scopus 로고    scopus 로고
    • Learning to detect natural image boundaries using local brightness, color, and texture cues
    • 1, 5
    • D. Martin, C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI, 2004.
    • (2004) PAMI
    • Martin, D.1    Fowlkes, C.2    Malik, J.3
  • 26
    • 71149084945 scopus 로고    scopus 로고
    • Deep learning from temporal coherence in video
    • 3
    • H. Mobahi, R. Collobert, and J. Weston. Deep learning from temporal coherence in video. In ICML, 2009.
    • (2009) ICML
    • Mobahi, H.1    Collobert, R.2    Weston, J.3
  • 28
  • 29
    • 84986308636 scopus 로고    scopus 로고
    • Video (language) modeling: A baseline for generative models of natural videos
    • 3
    • M. Ranzato, A. Szlam, J. Bruna, M. Mathieu, R. Collobert, and S. Chopra. Video (language) modeling: A baseline for generative models of natural videos. In ICLR, 2015.
    • (2015) ICLR
    • Ranzato, M.1    Szlam, A.2    Bruna, J.3    Mathieu, M.4    Collobert, R.5    Chopra, S.6
  • 30
    • 84877752264 scopus 로고    scopus 로고
    • Discriminatively trained sparse code gradients for contour detection
    • 1, 2
    • X. Ren and B. Liefeng. Discriminatively trained sparse code gradients for contour detection. In NIPS, 2012.
    • (2012) NIPS
    • Ren, X.1    Liefeng, B.2
  • 31
    • 84959237250 scopus 로고    scopus 로고
    • EpicFlow: Edge-preserving interpolation of correspondences for optical flow
    • 1, 2, 3, 4, 6
    • J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. In CVPR, 2015.
    • (2015) CVPR
    • Revaud, J.1    Weinzaepfel, P.2    Harchaoui, Z.3    Schmid, C.4
  • 35
    • 84911451086 scopus 로고    scopus 로고
    • Multiscale centerline detection by learning a scale-space distance transform
    • 2
    • A. Sironi, V. Lepetit, and P. Fua. Multiscale centerline detection by learning a scale-space distance transform. In CVPR, 2014.
    • (2014) CVPR
    • Sironi, A.1    Lepetit, V.2    Fua, P.3
  • 36
    • 84969544782 scopus 로고    scopus 로고
    • Unsupervised learning of video representations using LSTMs
    • 3
    • N. Srivastava, E. Mansimov, and R. Salakhutdinov. Unsupervised learning of video representations using LSTMs. In ICML, 2015.
    • (2015) ICML
    • Srivastava, N.1    Mansimov, E.2    Salakhutdinov, R.3
  • 38
    • 84867652321 scopus 로고    scopus 로고
    • Convolutional learning of spatio-temporal feat
    • 3
    • G. Taylor, R. Fergus, Y. LeCun, and C. Bregler. Convolutional learning of spatio-temporal feat. In ECCV, 2010.
    • (2010) ECCV
    • Taylor, G.1    Fergus, R.2    LeCun, Y.3    Bregler, C.4
  • 40
    • 0026240594 scopus 로고
    • Recognition by linear combinations of models
    • 1
    • S. Ullman and R. Basri. Recognition by linear combinations of models. PAMI, 1991.
    • (1991) PAMI
    • Ullman, S.1    Basri, R.2
  • 41
    • 84973889989 scopus 로고    scopus 로고
    • Unsupervised learning of visual representations using videos
    • 2, 3, 6
    • X. Wang and A. Gupta. Unsupervised learning of visual representations using videos. In ICCV, 2015.
    • (2015) ICCV
    • Wang, X.1    Gupta, A.2
  • 42
    • 84898830536 scopus 로고    scopus 로고
    • DeepFlow: Large displacement optical flow with deep matching
    • 2, 3
    • 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
  • 44
    • 84973859794 scopus 로고    scopus 로고
    • Holistically-nested edge detection
    • 1, 2, 4, 5, 7
    • S. Xie and Z. Tu. Holistically-nested edge detection. In ICCV, 2015.
    • (2015) ICCV
    • Xie, S.1    Tu, Z.2
  • 45
    • 85009899017 scopus 로고    scopus 로고
    • Visualizing and understanding convolutional networks
    • 7
    • M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
    • (2014) ECCV
    • Zeiler, M.D.1    Fergus, R.2
  • 46
    • 85009853104 scopus 로고    scopus 로고
    • Edge boxes: Locating object proposals from edges
    • 1, 4
    • C. L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In ECCV, 2014.
    • (2014) ECCV
    • Zitnick, C.L.1    Dollár, P.2
  • 47
    • 84867129630 scopus 로고    scopus 로고
    • The role of image understanding in contour detection
    • 1
    • C. L. Zitnick and D. Parikh. The role of image understanding in contour detection. In CVPR, 2012.
    • (2012) CVPR
    • Zitnick, C.L.1    Parikh, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.