Moving cast shadow detection using Physics-based features

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 2310-2317

  Huang, Jia Bin ^a   Chen, Chu Song ^a  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR; GAUSSIAN DISTRIBUTION; IMAGE SEGMENTATION; LIGHT SOURCES; PIXELS;

AMBIENT ILLUMINATION; DICHROMATIC REFLECTION MODEL; GAUSSIAN MIXTURE MODEL; ILLUMINATION CONDITIONS; MOVING CAST SHADOWS; SPECTRAL POWER DISTRIBUTION; STATISTICAL LEARNING; VISION APPLICATIONS;

COMPUTER VISION;

EID: 70450175532     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206629     Document Type: Conference Paper

References (16)

1. R. Cucchiara, C. Grana, M. Piccardi, and A. Prati. Detecting moving objects, ghosts, and shadows in video streams. IEEE Trans. PAMI, 25(10):1337-1342, 2003. 1.
2. T. Horprasert, D. Harwood, and L. S. Davis. A statistical approach for real-time robust background subtraction and shadow detection. In ICCV Frame-rate Workshop, 1999. 1.
3. D. S. Lee. Effective gaussian mixture learning for video background subtraction. IEEE Trans. PAMI, 27(5):827-832, 2005. 5.
4. Z. Liu, K. Huang, T. Tan, and L.Wang. Cast shadow removal combining local and global features. In CVPR, pages 1-8, 2007.
5. N. Martel-Brisson and A. Zaccarin. Learning and removing cast shadows through a multidistribution approach. IEEE Trans. PAMI, 29(7):1133-1146, 2007.
6. N. Martel-Brisson and A. Zaccarin. Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation. In CVPR, June. 2008.
7. B. Maxwell, R. Friedhoff, and C. Smith. A bi-illuminant dichromatic reflection model for understanding images. In CVPR, pages 1-8, June 2008.
8. S. Nadimi and B. Bhanu. Physical models for moving shadow and object detection in video. IEEE Trans. PAMI, 26(8):1079-1087, 2004.
9. F. Porikli and J. Thornton. Shadow flow: a recursive method to learn moving cast shadows. In ICCV, pages 891-898 Vol. 1, Oct. 2005.
10. A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara. Detecting moving shadows: algorithms and evaluation. IEEE Trans. PAMI, 25(7):918-923, 2003.
11. E. Salvador, A. Cavallaro, and T. Ebrahimi. Cast shadow segmentation using invariant color features. CVIU, 95(2):238- 259, 2004.
12. O. Schreer, I. Feldmann, U. Golz, and P. A. Kauff. Fast and robust shadow detection in videoconference applications. In IEEE Int'l Symp. Video/Image Processing and Multimedia Communications, pages 371-375, 2002.
13. S. Shafer. Using Color to Separate Reflection Components. Color Research Applications, pages 210-218, 1985.
14. J. Stander, R. Mech, and J. Ostermann. Detection of moving cast shadows for object segmentation. IEEE Trans. Multimedia, 1(1):65-76, Mar 1999.
15. C. Stauffer andW. E. L. Grimson. Adaptive background mixture models for real-time tracking. In Proc. CVPR, volume 2, pages -252, 1999.
16. W. Zhang, X. Fang, X. Yang, and Q. M. J. Wu. Moving cast shadows detection using ratio edge. IEEE Trans. Multimedia, 9(6):1202-1214, 2007.