Learning to recognize shadows in monochromatic natural images

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 223-230

  Zhu, Jiejie ^a   Samuel, Kegan G G ^a   Masood, Syed Z ^a   Tappen, Marshall F ^a  

^a University of Central Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHROMATIC INFORMATION; CONDITIONAL RANDOM FIELD; LOCAL CONSISTENCY; NATURAL IMAGES; NATURAL SCENES; QUANTITATIVE RESULT; SHADOW CLASSIFICATION;

COMPUTER VISION; DECISION TREES;

MONOCHROMATORS;

EID: 77955985942     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5540209     Document Type: Conference Paper

References (33)

1. E. Adelson. Image statistics and surface perception. In Human Vision and Electronic Imaging XIII, Proceedings of the SPIE, Volume 6806, pages 680602-680609, 2008. 3
2. E. Arbel and H. Hel-Ord. Texture-preserving shadow removal in color images containing curved surfaces. In CVPR, 2007. 1
3. V. Arévalo, J. González, and G. Ambrosio. Shadow detection in colour high-resolution satellite images. Int. J. Remote Sens., 29(7):1945-1963, 2008. 1
4. M. Bell and W. Freeman. Learning local evidence for shading and reflection. In ICCV, 2001. 1, 2
5. M. Collins, R. E. Schapire, and Y. Singer. Logistic regression, adaboost and bregman distances. In MACHINE LEARNING, pages 158-169, 2000. 5
6. R. Dror, A. Willsky, and E. Adelson. Statistical characterization of real-world illumination. Journal of Vision, 4(9):821-837, 2004. 2
7. M. A. T. Figueiredo. Bayesian image segmentation using gaussian field priors. In Energy Minimization Methods in Computer Vision and Pattern Recognition, 2005. 4
8. G. Finlayson, S. Hordley, C. Lu, and M. Drew. On the removal of shadows from images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(1):59-68, 2006. 1
9. A. D. Forsyth and M. Fleck. Identifying nude pictures. In IEEE Workshop on the Applications of Computer Vision, pages 103-108, 1996. 2
10. A. K. Frederick, C. Beauce, and L. Hunter. Colour vision brings clarity to shadows. Perception, 33(8):907-914, 2004. 1
11. T. Joachims, T. Finley, and C.-N. Yu. Cutting-plane training of structural svms. Machine Learning, 76(1), 2009. 4
12. A. Joshi and N. Papanikolopoulos. Learning to detect moving shadows in dynamic environments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11):2055-2063, 2008. 1
13. J. Kepner. Matlabmpi. J. Parallel Distrib. Comput., 64(8):997-1005, 2004. 5
14. D. Kersten, D. Knill, P. Mamassian, and I. B?ulthoff. Illusory motion from shadows. Nature, 379:31, 1996. 1
15. M. D. Levine and J. Bhattacharyya. Removing shadows. Pattern Recogn. Lett., 26(3):251-265, 2005. 1
16. N. Martel-Brisson and A. Zaccarin. Learning and removing cast shadows through a multidistribution approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(7):1133-1146, 2007. 1
17. D. R. Martin, C. C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5):530-549, 2004. 2, 3
18. K. McHenry, J. Ponce, and D. Forsyth. Finding glass. In CVPR, pages 973-979, Washington, DC, USA, 2005. IEEE Computer Society. 2
19. S. Nadimi and B. Bhanu. Physical models for moving shadow and object detection in video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8):1079-1087, 2004. 1
20. A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara. Detecting moving shadows: Algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(7):918-923, 2003. 2
21. J. Rittscher, J. Kato, S. Joga, and A. Blake. A probabilistic background model for tracking. In ECCV, pages 336-350, 2000. 1
22. B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. Labelme: A database and web-based tool for image annotation. Int. J. Comput. Vision, 77(1-3):157-173, 2008. 2
23. E. Salvador, A. Cavallaro, and T. Ebrahimi. Cast shadow segmentation using invariant color features. Comput. Vis. Image Underst., 95(2):238-259, 2004. 1
24. L. Shen, P. Tan, and S. Lin. Intrinsic image decomposition with nonlocal texture cues. In CVPR, 2008. 1
25. Y. Shor and D. Lischinski. The shadow meets the mask: Pyramid-based shadow removal. In European Association for Computer Graphics, 2008. 1
26. F. Tanner, B. Colder, C. Pullen, D. Heagy, C. Oertel, and P. Sallee. Overhead imagery research data set (oirds) an annotated data library and tools to aid in the development of computer vision algorithms, 2009. 2
27. M. Tappen, W. Freeman, and E. Adelson. Recovering intrinsic images from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9):1459-1472, 2005. 1, 2
28. M. Tappen, K. Samuel, C. Dean, and D. Lyle. The logistic random field - a convenient graphical model for learning parameters for mrf-based labeling. In CVPR, 2008. 2, 4
29. M. F. Tappen, E. H. Adelson, and W. T. Freeman. Estimating intrinsic component images using non-linear regression. In CVPR, pages 1992-1999, 2006. 2
30. B. Taskar, V. Chatalbashev, D. Koller, and C. Guestrin. Learning structured prediction models: A large margin approach. In The International Conference on Machine Learning, 2005. 4
31. Y. Wang, K. Loe, and J. Wu. A dynamic conditional random field model for foreground and shadow segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2):279-289, 2006. 1
32. Y. Weiss. Deriving intrinsic images from image sequences. In ICCV, pages 68-75, 2001. 1, 2
33. J. Winn and J. Shotton. The layout consistent random field for recognizing and segmenting partially occluded objects. In CVPR, pages 37-44, 2006. 5