메뉴 건너뛰기




Volumn , Issue , 2013, Pages 377-384

Semantic tie points

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC MAPPING; CAMERA ORIENTATION; CLASSIFICATION PERFORMANCE; IMAGE CORRESPONDENCE; MULTIPLE VIEWPOINTS; OVERLAPPING IMAGES; RANDOM FOREST CLASSIFIER; SEMANTIC INTERPRETATION;

EID: 84875594505     PISSN: 21583978     EISSN: 21583986     Source Type: Conference Proceeding    
DOI: 10.1109/WACV.2013.6475043     Document Type: Conference Paper
Times cited : (6)

References (28)
  • 1
    • 0001492549 scopus 로고    scopus 로고
    • Shape quantization and recognition with randomized trees
    • 2
    • Y. Amit and D. Geman. Shape quantization and recognition with randomized trees. Neural Computation, 9(7):1545-1588, 1997. 2
    • (1997) Neural Computation , vol.9 , Issue.7 , pp. 1545-1588
    • Amit, Y.1    Geman, D.2
  • 2
    • 0026868031 scopus 로고
    • Multispectral classification of landsat-images using neural networks
    • 2
    • H. Bischof,W. Schneider, and A. J. Pinz. Multispectral classification of landsat-images using neural networks. IEEE T. Geoscience and Remote Sensing, 30(3):482-490, 1992. 2
    • (1992) IEEE T. Geoscience and Remote Sensing , vol.30 , Issue.3 , pp. 482-490
    • Bischof, H.1    Schneider, W.2    Pinz, A.J.3
  • 3
    • 50649101132 scopus 로고    scopus 로고
    • Image classification using random forests and ferns
    • 2
    • A. Bosch, A. Zisserman, and X. Munoz. Image classification using random forests and ferns. In ICCV, 2007. 2
    • (2007) ICCV
    • Bosch, A.1    Zisserman, A.2    Munoz, X.3
  • 4
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • 1, 2
    • L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001. 1, 2
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 5
    • 33750819329 scopus 로고    scopus 로고
    • A novel transductive SVM for semisupervised classification of remotesensing images
    • 2
    • L. Bruzzone, M. Chi, and M. Marconcini. A novel transductive SVM for semisupervised classification of remotesensing images. IEEE T. Geoscience and Remote Sensing, 44(1-2):3363-3373, 2006. 2
    • (2006) IEEE T. Geoscience and Remote Sensing , vol.44 , Issue.1-2 , pp. 3363-3373
    • Bruzzone, L.1    Chi, M.2    Marconcini, M.3
  • 6
    • 84896797897 scopus 로고    scopus 로고
    • Extracting structures in image collections for object recognition
    • 2
    • S. Ebert, D. Larlus, and B. Schiele. Extracting structures in image collections for object recognition. In ECCV, 2010. 2
    • (2010) ECCV
    • Ebert, S.1    Larlus, D.2    Schiele, B.3
  • 7
    • 51949103533 scopus 로고    scopus 로고
    • A mobile vision system for robust multi-person tracking
    • 1
    • A. Ess, B. Leibe, K. Schindler, and L. V. Gool. A mobile vision system for robust multi-person tracking. In CVPR, 2008. 1
    • (2008) CVPR
    • Ess, A.1    Leibe, B.2    Schindler, K.3    Gool, L.V.4
  • 8
    • 33750397657 scopus 로고    scopus 로고
    • Weakly supervised scale-invariant learning of models for visual recognition
    • 2
    • R. Fergus, P. Perona, and A. Zisserman. Weakly supervised scale-invariant learning of models for visual recognition. IJCV, 71(3):273-303, 2007. 2
    • (2007) IJCV , vol.71 , Issue.3 , pp. 273-303
    • Fergus, R.1    Perona, P.2    Zisserman, A.3
  • 9
    • 77955655063 scopus 로고    scopus 로고
    • Semi-supervised learning in gigantic image collections
    • 2
    • R. Fergus, Y.Weiss, and A. Torralba. Semi-supervised learning in gigantic image collections. In NIPS, 2009. 2
    • (2009) NIPS
    • Fergus, R.1    Weiss, Y.2    Torralba, A.3
  • 10
    • 70450201402 scopus 로고    scopus 로고
    • Class-specific hough forests for object detection
    • 2
    • J. Gall and V. S. Lempitsky. Class-specific hough forests for object detection. In CVPR, 2009. 2
    • (2009) CVPR
    • Gall, J.1    Lempitsky, V.S.2
  • 12
    • 0030380542 scopus 로고    scopus 로고
    • Color and texture fusion: Application to aerial image segmentation and GIS updating
    • 2
    • M.-P. D. Jolly. Color and texture fusion: application to aerial image segmentation and GIS updating. In WACV, 1996. 2
    • (1996) WACV
    • Jolly, M.-P.D.1
  • 13
    • 79959696781 scopus 로고    scopus 로고
    • Semantic classification in aerial imagery by integrating appearance and height information
    • 1, 2
    • S. Kluckner, T. Mauthner, P. M. Roth, and H. Bischof. Semantic classification in aerial imagery by integrating appearance and height information. In ACCV, 2009. 1, 2
    • (2009) ACCV
    • Kluckner, S.1    Mauthner, T.2    Roth, P.M.3    Bischof, H.4
  • 14
    • 51949110976 scopus 로고    scopus 로고
    • Object categorization using co-occurrence, location and appearance
    • 1
    • C. Galleguillos, A. Rabinovich, and S. Belongie. Object categorization using co-occurrence, location and appearance. In CVPR, 2008. 1
    • (2008) CVPR
    • Galleguillos, C.1    Rabinovich, A.2    Belongie, S.3
  • 15
    • 58149151266 scopus 로고    scopus 로고
    • Texton-boost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context
    • 1
    • J. Shotton, J.M. Winn, C. Rother, and A. Criminisi. Texton-Boost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context. In IJCV, 81(1):2-23, 2009. 1
    • (2009) IJCV , vol.81 , Issue.1 , pp. 2-23
    • Shotton, J.1    Winn, J.M.2    Rother, C.3    Criminisi, A.4
  • 16
    • 39749187303 scopus 로고    scopus 로고
    • Object extraction in photogrammetric computer vision
    • 1
    • H. Mayer. Object extraction in photogrammetric computer vision. In IJPRS, 63(2):213-222, 2008. 1
    • (2008) IJPRS , vol.63 , Issue.2 , pp. 213-222
    • Mayer, H.1
  • 19
    • 33745821131 scopus 로고    scopus 로고
    • Keypoint recognition using randomized trees
    • 2
    • V. Lepetit and P. Fua. Keypoint recognition using randomized trees. IEEE PAMI, 28(9):1465-1479, 2006. 2
    • (2006) IEEE PAMI , vol.28 , Issue.9 , pp. 1465-1479
    • Lepetit, V.1    Fua, P.2
  • 20
    • 4344614511 scopus 로고    scopus 로고
    • Classification of hyperspectral remote sensing images with support vector machines
    • 2
    • F. Melgani and L. Bruzzone. Classification of hyperspectral remote sensing images with support vector machines. IEEE T. Geoscience and Remote Sensing, 42(8):1778-1790, 2004. 2
    • (2004) IEEE T. Geoscience and Remote Sensing , vol.42 , Issue.8 , pp. 1778-1790
    • Melgani, F.1    Bruzzone, L.2
  • 21
    • 84055175817 scopus 로고    scopus 로고
    • Learning to detect roads in highresolution aerial images
    • 2
    • V. Mnih and G. E. Hinton. Learning to detect roads in highresolution aerial images. In ECCV, 2010. 2
    • (2010) ECCV
    • Mnih, V.1    Hinton, G.E.2
  • 22
    • 21944442484 scopus 로고    scopus 로고
    • Weak hypotheses and boosting for generic object detection and recognition
    • 2
    • A. Opelt, M. Fussenegger, A. Pinz, and P. Auer. Weak hypotheses and boosting for generic object detection and recognition. In ECCV, 2004. 2
    • (2004) ECCV
    • Opelt, A.1    Fussenegger, M.2    Pinz, A.3    Auer, P.4
  • 24
    • 70450219022 scopus 로고    scopus 로고
    • Regularized multiclass semi-supervised boosting
    • 2
    • A. Saffari, C. Leistner, and H. Bischof. Regularized multiclass semi-supervised boosting. In CVPR, 2009. 2
    • (2009) CVPR
    • Saffari, A.1    Leistner, C.2    Bischof, H.3
  • 25
    • 51949114829 scopus 로고    scopus 로고
    • Semantic texton forests for image categorization and segmentation
    • 2
    • J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. In CVPR, 2008. 2
    • (2008) CVPR
    • Shotton, J.1    Johnson, M.2    Cipolla, R.3
  • 26
    • 50649119191 scopus 로고    scopus 로고
    • Object categorization by learned universal visual dictionary
    • 5
    • J. Winn, A. Criminisi, and T. Minka. Object categorization by learned universal visual dictionary. In CVPR, 2005. 5
    • (2005) CVPR
    • Winn, J.1    Criminisi, A.2    Minka, T.3
  • 27
    • 80052878231 scopus 로고    scopus 로고
    • Monocular 3d scene modeling and inference: Understanding multiobject traffic scenes
    • 1
    • C. Wojek, S. Roth, K. Schindler, and B. Schiele. Monocular 3d scene modeling and inference: understanding multiobject traffic scenes. In ECCV, 2010. 1
    • (2010) ECCV
    • Wojek, C.1    Roth, S.2    Schindler, K.3    Schiele, B.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.