Probability models for high dynamic range imaging

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

Volumn 2, Issue , 2004, Pages

  Pal, Chris ^a   Szeliski, Rick ^a   Uyttendaele, Matthew ^a   Jojic, Nebojsa ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL PHOTOGRAPHY; HIGH DYNAMIC RANGE (HDR); IMAGING FUNCTIONS; INTENSITY TRANSFORMATION;

CAMERAS; CHARGE COUPLED DEVICES; DIGITAL SIGNAL PROCESSING; FUNCTIONS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PHOTOGRAPHY; PROBABILITY;

IMAGING TECHNIQUES;

EID: 5044222776     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. P. E. Debevec and J. Malik. Recovering high dynamic range radiance maps from photographs. Computer Graphics, 31(Annual Conference Series, SIGGRAPH 1997):369-378, 1997.
2. M. D. Grossberg and S.K. Nayar. What can be known about the radiometric response from images? Proceedings of the European Conference on Computer Vision (ECCV), 4:189-205, 2002.
3. M. D. Grossberg and S.K. Nayar. What is the space of camera response functions? Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2:602-609, 2003.
4. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer Series in Statistics. Springer-Verlag, New York, 2001.
5. G.S. Kimeldorf and G. Wahba. A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. The Annals of Mathematical Statistics, 41(2):495-502, 1970.
6. S. Mann. Comparametric equations with practical applications in quantigraphic image processing. IEEE Trans. Image Proc., 9(8): 1389-1406, August 2000.
7. S. Mann and R. Picard. Being 'undigital' with digital cameras: Extending dynamic range by combining differently exposed pictures. In Proceedings of IST 46th annual conference, pages 422-428, May 1995.
8. T. Mitsunaga and S. K. Nayar, Radiometric self calibration. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (ICCV), June 1999.
9. R. Neal. Bayesian Learning for Neural Networks. Springer-Verlag, New York, 1996.
10. E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda. Photographic tone reproduction for digital images. Proceedings of ACM SIGGRAPH, pages 267-276, 2002.
11. Y. Tsin, V. Ramesh, and T. Kanade. Statistical calibration of CCD imaging process. Proceedings of IEEE International Conference on Computer Vision (ICCV), July 2001.
12. G. Wahba. Spline Models for Observational Data. volume 59 of CBMS-NSF regional conference series in applied mathematics.SIAM, 1990.