What do color changes reveal about an outdoor scene?

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Sunkavalli, Kalyan ^a   Romeiro, Fabiano ^a   Matusik, Wojciech ^b   Zickler, Todd ^a   Pfister, Hanspeter ^a  

^a HARVARD UNIVERSITY   (United States)

^b ADOBE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND SUBTRACTION; COLOR CHANGING; GEO-LOCATION; GEOMETRIC INFORMATION; IMAGE SEQUENCING; OUTDOOR SCENES; SCENE RECONSTRUCTION; SHADOW DETECTIONS; SPECTRAL COMPOSITION; TIME-VARYING CHANGES; VIDEO DATA; VISUAL TASKS;

ARTIFICIAL INTELLIGENCE; ATMOSPHERICS; COLOR; COLORIMETRY; COMPUTER VISION; FEATURE EXTRACTION; IMAGE PROCESSING; LIGHT MEASUREMENT; LIGHTING; PATTERN RECOGNITION; VIDEO RECORDING;

OPTICAL PROPERTIES;

EID: 51949108663     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587411     Document Type: Conference Paper

References (19)

1. K. Barnard, G. D. Finlayson, and B. V. Funt. Color constancy for scenes with varying illumination. Computer Vision and Image Understanding, 65(2):311-321, March 1997.
2. H. Barrow and J. Tenenbaum. Recovering intrinsic scene characteristics from images. Academic Press, 1978.
3. Y. Boykov and V. Kolmogorov. An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision. 26(9):1124-1137, 2004.
4. H. Y. Chong, S. J. Gortler, and T. Zickler. The von Kries hypothesis and a basis for color constancy. In Proc. IEEE Int. Conf. Computer Vision, 2007.
5. F. Cozman and E. Krotkov. Robot localization using a computer vision sextant. In Proc. IEEE Conf. Robotics and Automation, pages 106-111, 1995.
6. M. Ebner. Color constancy using local color shifts. In Proc. European Conf. Computer Vision, pages 276-287, May 2004.
7. G. Finlayson, M. Drew, and B. Funt. Color constancy: generalized diagonal transforms suffice. J. Optical Society of America A, 11(11):3011-3019, 1994.
8. G. D. Finlayson, M. S. Drew, and C. Lu. Intrinsic images by entropy minimization. In 8th European Conference on Computer Vision (ECCV 2004), pages 582-595, May 2004.
9. G. D. Finlayson, S. D. Hordley, and M. S. Drew. Removing shadows from images. In Proc. European Conf. Computer Vision, pages 823-836, May 2002.
10. R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2000.
11. N. Jacobs, S. Satkin, N. Roman, R. Speyer, and R. Pless. Geolocating static cameras. In Proc. IEEE Int. Conf. Computer Vision, 2007.
12. Y. Matsushita, K. Nishino, K. Ikeuchi, and M. Sakauchi. Illumination normalization with time-dependent intrinsic images for video surveillance. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 3-10, 2003.
13. M. Oren and S. Nayar. Generalization of the Lambertian model and implications for machine vision. Int. Journal of Computer Vision, 14:227-251, 1996.
14. K. Sunkavalli, W. Matusik, H. Pfister, and S. Rusinkiewicz. Factored time-lapse video. ACM Transactions on Graphics, 26(3):101:1-101:10, July 2007.
15. M. F. Tappen, W. T. Freeman, and E. H. Adelson. Recovering intrinsic images from a single image. In Neural Information Processing Systems, pages 1343-1350, December 2002.
16. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principles and practice of background maintenance. In Proc. IEEE Int. Conf. Computer Vision, pages 255-261, 1999.
17. A. Trebi-Ollennu, T. Huntsberger, Y. Cheng, E. T. Baumgartner, B. Kennedy, and P. Schenker. Design and analysis of a sun sensor for planetary rover absolute heading detection. IEEE Trans. on Robotics and Automation, 17(6), 2001.
18. Y. Weiss. Deriving Intrinsic Images from Image Sequences. In Proc. IEEE Int. Conf. Computer Vision, pages II: 68-75, 2001.
19. G. Wyszecki and W. Stiles. Color Science: Concepts and Methods, Quantitative Data and Formulae. Wiley New York, second edition, 2000.