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Volumn , Issue , 2013, Pages 2985-2992

Spatial inference machines

Author keywords

3D point clouds; computer vision; depth images; inference machines; scene understanding; semantic segmentation

Indexed keywords

3D POINT CLOUD; DEPTH IMAGE; INFERENCE MACHINE; SCENE UNDERSTANDING; SEMANTIC SEGMENTATION;

EID: 84887355850     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.384     Document Type: Conference Paper
Times cited : (34)

References (21)
  • 1
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • 3, 6
    • L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001. 3, 6
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 2
    • 0002184557 scopus 로고
    • A simple rule-based part of speech tagger
    • Trento, IT 1
    • E. Brill. A simple rule-based part of speech tagger. In ACL, pages 112-116, Trento, IT, 1992. 1
    • (1992) ACL , pp. 112-116
    • Brill, E.1
  • 3
    • 77953180995 scopus 로고    scopus 로고
    • Discriminative models for multi-class object layout
    • 2, 5
    • C. Desai, D. Ramanan, and C. Fowlkes. Discriminative models for multi-class object layout. In ICCV, pages 229-236, 2009. 2, 5
    • (2009) ICCV , pp. 229-236
    • Desai, C.1    Ramanan, D.2    Fowlkes, C.3
  • 4
    • 80052913933 scopus 로고    scopus 로고
    • Parameter Learning with Truncated Message-Passing
    • Colorado Springs, CO 2
    • J. Domke. Parameter Learning with Truncated Message-Passing. In CVPR, number x, pages 2937-2944, Colorado Springs, CO, 2011. 2
    • (2011) CVPR , Issue.10 , pp. 2937-2944
    • Domke, J.1
  • 5
    • 85078986900 scopus 로고    scopus 로고
    • Class segmentation and object localization with superpixel neighborhoods
    • 1
    • B. Fulkerson, A. Vedaldi, and S. Soatto. Class segmentation and object localization with superpixel neighborhoods. In ICCV, pages 670-677, 2009. 1
    • (2009) ICCV , pp. 670-677
    • Fulkerson, B.1    Vedaldi, A.2    Soatto, S.3
  • 6
    • 56749104177 scopus 로고    scopus 로고
    • Learning spatial context: Using stuff to find things
    • Marseille, France 2, 5
    • G. Heitz and D. Koller. Learning spatial context: Using stuff to find things. In ECCV, pages 30-43, Marseille, France, 2008. 2, 5
    • (2008) ECCV , pp. 30-43
    • Heitz, G.1    Koller, D.2
  • 7
    • 61349174704 scopus 로고    scopus 로고
    • Robust higher order potentials for enforcing label consistency
    • Jan. 1
    • P. Kohli, L. Ladický, and P. H. S. Torr. Robust Higher Order Potentials for Enforcing Label Consistency. IJCV, 82(3):302-324, Jan. 2009. 1
    • (2009) IJCV , vol.82 , Issue.3 , pp. 302-324
    • Kohli, P.1    Ladický, L.2    Torr, P.H.S.3
  • 8
    • 85162558717 scopus 로고    scopus 로고
    • Semantic labeling of 3d point clouds for indoor scenes
    • Granada, ES 1, 2, 5, 6, 7, 8
    • H. S. Koppula, A. Anand, T. Joachims, and A. Saxena. Semantic Labeling of 3D Point Clouds for Indoor Scenes. In NIPS, Granada, ES, 2011. 1, 2, 5, 6, 7, 8
    • (2011) NIPS
    • Koppula, H.S.1    Anand, A.2    Joachims, T.3    Saxena, A.4
  • 9
    • 85162351107 scopus 로고    scopus 로고
    • Efficient inference in fully connected crfs with gaussian edge potentials
    • 1
    • P. Krähenbühl and V. Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In NIPS, 2011. 1
    • (2011) NIPS
    • Krähenbühl, P.1    Koltun, V.2
  • 10
  • 12
    • 70450162267 scopus 로고    scopus 로고
    • Contextual classification with functional Max-Margin Markov Networks
    • Miami, FL, June. 1, 2
    • D. Munoz, J. A. Bagnell, N. Vandapel, and M. Hebert. Contextual classification with functional Max-Margin Markov Networks. In CVPR, pages 975-982, Miami, FL, June 2009. 1, 2
    • (2009) CVPR , pp. 975-982
    • Munoz, D.1    Bagnell, J.A.2    Vandapel, N.3    Hebert, M.4
  • 13
    • 70450184098 scopus 로고    scopus 로고
    • Global connectivity potentials for random field models
    • June. 1
    • S. Nowozin and C. H. Lampert. Global connectivity potentials for random field models. In CVPR, pages 818-825, June 2009. 1
    • (2009) CVPR , pp. 818-825
    • Nowozin, S.1    Lampert, C.H.2
  • 15
    • 80052872903 scopus 로고    scopus 로고
    • Learning message-passing inference machines for structured prediction
    • Colorado Springs, CO 1, 2, 3, 4, 8
    • S. Ross, D. Munoz, M. Hebert, and J. A. Bagnell. Learning Message-Passing Inference Machines for Structured Prediction. In CVPR, pages 2737-2744, Colorado Springs, CO, 2011. 1, 2, 3, 4, 8
    • (2011) CVPR , pp. 2737-2744
    • Ross, S.1    Munoz, D.2    Hebert, M.3    Bagnell, J.A.4
  • 16
    • 80051960494 scopus 로고    scopus 로고
    • Cutting-plane training of nonassociative markov network for 3d point cloud segmentation
    • Hangzhou, China 2
    • R. Shapovalov and A. Velizhev. Cutting-Plane Training of Nonassociative Markov Network for 3D Point Cloud Segmentation. In 3DIMPVT, pages 1-8, Hangzhou, China, 2011. 2
    • (2011) 3DIMPVT , pp. 1-8
    • Shapovalov, R.1    Velizhev, A.2
  • 17
    • 51949114829 scopus 로고    scopus 로고
    • Semantic texton forests for image categorization and segmentation
    • June. 1, 2
    • J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. In CVPR, June 2008. 1, 2
    • (2008) CVPR
    • Shotton, J.1    Johnson, M.2    Cipolla, R.3
  • 19
    • 51949119486 scopus 로고    scopus 로고
    • Auto-context and its application to high-level vision tasks
    • Anchorage, AL, June 1, 2
    • Z. Tu. Auto-context and its application to high-level vision tasks. In CVPR, Anchorage, AL, June 2008. 1, 2
    • (2008) CVPR
    • Tu, Z.1
  • 20
    • 77953223384 scopus 로고    scopus 로고
    • A global perspective on MAP inference for low-level vision
    • ICCV. 1
    • O. J. Woodford, C. Rother, and V. Kolmogorov. A global perspective on MAP inference for low-level vision. In ICCV, number Iccv, pages 2319-2326. ICCV, 2009. 1
    • (2009) ICCV, Number Iccv , pp. 2319-2326
    • Woodford, O.J.1    Rother, C.2    Kolmogorov, V.3
  • 21
    • 84860639682 scopus 로고    scopus 로고
    • 3-d scene analysis via sequenced predictions over points and regions
    • Shanghai, China 1
    • X. Xiong, D. Munoz, J. A. Bagnell, and M. Hebert. 3-D Scene Analysis via Sequenced Predictions over Points and Regions. In ICRA, Shanghai, China, 2011. 1
    • (2011) ICRA
    • Xiong, X.1    Munoz, D.2    Bagnell, J.A.3    Hebert, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.