메뉴 건너뛰기




Volumn 8753, Issue , 2014, Pages 107-118

A deep variational model for image segmentation

Author keywords

[No Author keywords available]

Indexed keywords

VARIATIONAL MODELING;

EID: 84908701435     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-11752-2_9     Document Type: Conference Paper
Times cited : (19)

References (33)
  • 1
    • 77955996057 scopus 로고    scopus 로고
    • Fast and robust object segmentation with the integral linear classifier
    • Aldavert, D., Ramisa, A., de Mantaras, R.L., Toledo, R.: Fast and robust object segmentation with the integral linear classifier. In: CVPR (2010)
    • (2010) CVPR
    • Aldavert, D.1    Ramisa, A.2    De Mantaras, R.L.3    Toledo, R.4
  • 2
    • 84889067358 scopus 로고    scopus 로고
    • Semantic road segmentation via multi-scale ensembles of learned features
    • Alvarez, J.M., LeCun, Y., Gevers, T., Lopez, A.: Semantic road segmentation via multi-scale ensembles of learned features. In: ECCV Workshops (2012)
    • (2012) ECCV Workshops
    • Alvarez, J.M.1    Lecun, Y.2    Gevers, T.3    Lopez, A.4
  • 3
    • 80052894155 scopus 로고    scopus 로고
    • Kernelized structural svm learning for supervised object segmentation
    • Bertelli, L., Yu, T., Vu, D., Gokturk, B.: Kernelized structural svm learning for supervised object segmentation. In: CVPR (2011)
    • (2011) CVPR
    • Bertelli, L.1    Yu, T.2    Vu, D.3    Gokturk, B.4
  • 4
    • 84932617867 scopus 로고    scopus 로고
    • Combining top-down and bottom-up segmentation
    • Borenstein, E., Sharon, E., Ullman, S.: Combining top-down and bottom-up segmentation. In: CVPR (2004)
    • (2004) CVPR
    • Borenstein, E.1    Sharon, E.2    Ullman, S.3
  • 5
    • 0030648914 scopus 로고    scopus 로고
    • Global training of document processing systems using graph transformer networks
    • IEEE, Puerto-Rico
    • Bottou, L., Le Cun, Y., Bengio, Y.: Global training of document processing systems using graph transformer networks. In: Proceedings of Computer Vision and Pattern Recognition, pp. 489–493. IEEE, Puerto-Rico (1997)
    • (1997) Proceedings of Computer Vision and Pattern Recognition , pp. 489-493
    • Bottou, L.1    Le Cun, Y.2    Bengio, Y.3
  • 6
    • 0034844730 scopus 로고    scopus 로고
    • Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
    • Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: ICCV (2001)
    • (2001) ICCV
    • Boykov, Y.Y.1    Jolly, M.P.2
  • 7
    • 84885591253 scopus 로고    scopus 로고
    • Training energy-based models for timeseries imputation
    • Brakel, P., Stroobandt, D., Schrauwen, B.: Training energy-based models for timeseries imputation. J. of Mach. Learn. Res. 14, 2771–2797 (2013)
    • (2013) J. of Mach. Learn. Res , vol.14 , pp. 2771-2797
    • Brakel, P.1    Stroobandt, D.2    Schrauwen, B.3
  • 8
    • 67349189437 scopus 로고    scopus 로고
    • On total variation minimization and surface evolution using parametric maximum flows
    • Chambolle, A., Darbon, J.: On total variation minimization and surface evolution using parametric maximum flows. IJCV 84(3), 288–307 (2009)
    • (2009) IJCV , vol.84 , Issue.3 , pp. 288-307
    • Chambolle, A.1    Darbon, J.2
  • 9
    • 33750153530 scopus 로고    scopus 로고
    • Algorithms for finding global minimizers of image segmentation and denoising models
    • Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. J. App. Math. 66, 1632–1648 (2004)
    • (2004) J. App. Math , vol.66 , pp. 1632-1648
    • Chan, T.F.1    Esedoglu, S.2    Nikolova, M.3
  • 10
    • 84862599064 scopus 로고    scopus 로고
    • Learning spectral graph segmentation
    • Cour, T., Gogin, N., Shi, J.: Learning spectral graph segmentation. In: AISTATS (2005)
    • (2005) AISTATS
    • Cour, T.1    Gogin, N.2    Shi, J.3
  • 11
    • 84869036002 scopus 로고    scopus 로고
    • Generic methods for optimization-based modeling
    • Domke, J.: Generic methods for optimization-based modeling. J. Mach. Learn. Res. 22, 318–326 (2012)
    • (2012) J. Mach. Learn. Res , vol.22 , pp. 318-326
    • Domke, J.1
  • 12
    • 84867136939 scopus 로고    scopus 로고
    • Scene parsing with multiscale feature learning, purity trees, and optimal covers
    • Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Scene parsing with multiscale feature learning, purity trees, and optimal covers. In: ICML (2012)
    • (2012) ICML
    • Farabet, C.1    Couprie, C.2    Najman, L.3    Lecun, Y.4
  • 13
    • 85078986900 scopus 로고    scopus 로고
    • Class segmentation and object localization with superpixel neighborhoods
    • Fulkerson, B., Vedaldi, A., Soatto, S.: Class segmentation and object localization with superpixel neighborhoods. In: ICCV (2009)
    • (2009) ICCV
    • Fulkerson, B.1    Vedaldi, A.2    Soatto, S.3
  • 14
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • Hinton, G.: Training products of experts by minimizing contrastive divergence. Neur. Comput. 14, 1771–1800 (2000)
    • (2000) Neur. Comput , vol.14 , pp. 1771-1800
    • Hinton, G.1
  • 15
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • Hinton, G., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neur. Comput. 18, 1527–1554 (2006)
    • (2006) Neur. Comput , vol.18 , pp. 1527-1554
    • Hinton, G.1    Osindero, S.2    Teh, Y.W.3
  • 16
    • 84991741460 scopus 로고    scopus 로고
    • Natural image denoising with convolutional networks
    • Jain, V., Seung, H.S.: Natural image denoising with convolutional networks. In: NIPS (2008)
    • (2008) NIPS
    • Jain, V.1    Seung, H.S.2
  • 17
    • 84866674114 scopus 로고    scopus 로고
    • Regression tree fields - an efficient, non-parametric approach to image labeling problems
    • Jancsary, J., Nowozin, S., Sharp, T., Rother, C.: Regression tree fields - an efficient, non-parametric approach to image labeling problems. In: CVPR (2012)
    • (2012) CVPR
    • Jancsary, J.1    Nowozin, S.2    Sharp, T.3    Rother, C.4
  • 18
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 19
    • 84866697047 scopus 로고    scopus 로고
    • Figure-ground segmentation by transferring window masks
    • Kuettel, D., Ferrari, V.: Figure-ground segmentation by transferring window masks. In: CVPR (2012)
    • (2012) CVPR
    • Kuettel, D.1    Ferrari, V.2
  • 22
    • 58049184640 scopus 로고    scopus 로고
    • Learning to combine bottom-up and top-down segmentation
    • Levin, A., Weiss, Y.: Learning to combine bottom-up and top-down segmentation. IJCV 81(1), 105–118 (2009)
    • (2009) IJCV , vol.81 , Issue.1 , pp. 105-118
    • Levin, A.1    Weiss, Y.2
  • 23
    • 33646887390 scopus 로고
    • On the limited memory BFGS method for large scale optimization
    • Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45, 503–528 (1989)
    • (1989) Math. Program , vol.45 , pp. 503-528
    • Liu, D.C.1    Nocedal, J.2
  • 24
    • 34948909658 scopus 로고    scopus 로고
    • Accurate object localization with shape masks
    • Marszalek, M., Schmid, C.: Accurate object localization with shape masks. In: CVPR (2007)
    • (2007) CVPR
    • Marszalek, M.1    Schmid, C.2
  • 25
    • 84879800501 scopus 로고    scopus 로고
    • Gradient methods for minimizing composite objective function
    • Nesterov, Y.: Gradient methods for minimizing composite objective function. Math. Program. 140, 125–161 (2013)
    • (2013) Math. Program , vol.140 , pp. 125-161
    • Nesterov, Y.1
  • 27
    • 31544472083 scopus 로고    scopus 로고
    • Generic object recognition with boosting
    • Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. PAMI 28, 416–431 (2004)
    • (2004) PAMI , vol.28 , pp. 416-431
    • Opelt, A.1    Pinz, A.2    Fussenegger, M.3    Auer, P.4
  • 28
    • 70450194857 scopus 로고    scopus 로고
    • A convex relaxation approach for computing minimal partitions
    • Pock, T., Chambolle, A., Cremers, D., Bischof, H.: A convex relaxation approach for computing minimal partitions. In: CVPR (2009)
    • (2009) CVPR
    • Pock, T.1    Chambolle, A.2    Cremers, D.3    Bischof, H.4
  • 29
    • 70450207702 scopus 로고    scopus 로고
    • Learning optimized map estimates in continuouslyvalued mrf models
    • Samuel, K.G.G., Tappen, M.F.: Learning optimized map estimates in continuouslyvalued mrf models. In: CVPR (2009)
    • (2009) CVPR
    • Samuel, K.G.G.1    Tappen, M.F.2
  • 30
    • 80054736963 scopus 로고    scopus 로고
    • Traffic sign recognition with multi-scale convolutional networks
    • Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: IJCNN, pp. 2809–2813 (2011)
    • (2011) IJCNN , pp. 2809-2813
    • Sermanet, P.1    Lecun, Y.2
  • 31
    • 51949118679 scopus 로고    scopus 로고
    • The logistic random field - a convenient graphical model for learning parameters for mrf-based labeling
    • Tappen, M.F., Samuel, K.G.G., Dean, C.V., Lyle, D.M.: The logistic random field - a convenient graphical model for learning parameters for mrf-based labeling. In: CVPR (2008)
    • (2008) CVPR
    • Tappen, M.F.1    Samuel, K.G.G.2    Dean, C.V.3    Lyle, D.M.4
  • 32
    • 24944537843 scopus 로고    scopus 로고
    • Large margin methods for structured and interdependent output variables
    • Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res. 6, 1453–1484 (2005)
    • (2005) J. Mach. Learn. Res , vol.6 , pp. 1453-1484
    • Tsochantaridis, I.1    Joachims, T.2    Hofmann, T.3    Altun, Y.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.