This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Radiometric CCD camera calibration and noise estimation
March 1994 (vol. 16 no. 3)
pp. 267-276

Changes in measured image irradiance have many physical causes and are the primary cue for several visual processes, such as edge detection and shape from shading. Using physical models for charged-coupled device (CCD) video cameras and material reflectance, we quantify the variation in digitized pixel values that is due to sensor noise and scene variation. This analysis forms the basis of algorithms for camera characterization and calibration and for scene description. Specifically, algorithms are developed for estimating the parameters of camera noise and for calibrating a camera to remove the effects of fixed pattern nonuniformity and spatial variation in dark current. While these techniques have many potential uses, we describe in particular how they can be used to estimate a measure of scene variation. This measure is independent of image irradiance and can be used to identify a surface from a single sensor band over a range of situations. Experimental results confirm that the models presented in this paper are useful for modeling the different sources of variation in real images obtained from video cameras.

[1] G. Amelio, M. Tompsett, and G. Smith, "Experimental verification of the charge coupled device concept,"Bell Syst. Tech. J., vol. 49, pp. 593-600, Apr. 1970.
[2] P. Besl and R. Jain, "Segmentation through variable-order surface fitting,"IEEE Trans. Pattern Anal. Machine Intell., vol. 10, no. 2, pp. 167-192, Mar. 1988.
[3] P. Bevington,Data Reduction and Error Analysis for the Physical Sciences. New York: McGraw-Hill, 1969.
[4] H. Beyer, "Linejitter and geometric calibration of CCD cameras,"ISPRS J. Photogrammetry and Remote Sensing, vol. 45, pp. 17-32, 1990.
[5] M. Blouke and J. Janesick,Arms Control Verification: The Technologies That Make It Possible. 1987.
[6] W. Boyle and G. Smith, "Charge coupled semiconductor devices,"Bell Syst. Tech. J., vol. 49, pp. 587-593, Apr. 1970.
[7] J. Carnes and W. Kosonocky, "Noise sources in charge-coupled devices"RCA Rev., vol. 33, pp. 327-343, June 1972.
[8] M. Collet, "Solid-state image sensors,"Sensors and Actuators, vol. 10, pp. 287-302, 1986.
[9] G. Healey, "Using color for geometry insensitive segmentation,"J. Opt. Soc. Am. A, vol. 6, no. 6, pp. 920-937, June 1989.
[10] B. K. P. Horn, "Shape from shading: A method for obtaining the shape of a smooth opaque object from one view," MIT Project MAC Int. Rep. TR-79 and MIT AI Lab, Tech. Rep. 232, Nov. 1970.
[11] F. Huck, N. Halyo, and S. Park, "Aliasing and blurring in 2-D sampled imagery,"Appl. Opt., vol. 19, no. 23, pp. 2174-2181, July 1980.
[12] K. Ikeuchi and T. Kanade, "Modeling sensors: Toward automatic generation of object recognition program,"Comp. Vision, Graphics, Image Processing, vol. 48, pp. 50-79, 1989.
[13] J. Janesick, T. Elliott, S. Collins, M. Blouke, and J. Freeman, "Scientific charge-coupled devices,"Opt. Eng., vol. 26, no. 8, pp. 692-715, Aug. 1987.
[14] G. J. Klinker, S. A. Shafer, and T.Kanade, "A physical approach to color image understanding,"Int. J. Comput. Vision, vol. 4, no. 1, pp. 7-38, 1990.
[15] J. Kristian and M. Blouke, "Charge-coupled devices in astronomy,"Scientific American, vol. 247, pp. 67-74, Oct. 1982.
[16] J. Krumm and S.A. Shafer, "Sampled-grating and crossed-grating models of moire patterns from digital imaging,"Opt. Eng., vol. 30, no. 2, pp. 195-206, Feb. 1991.
[17] H.-C. Lee, E. Breneman, and C. Schulte, "Modeling light reflection for computer color vision,"IEEE Trans. Pattern Anal. Machine Intell., vol. 12, no. 4, pp. 402-409, Apr. 1990.
[18] R. Lenz and D. Fritsch, "Accuracy of videometry with CCD sensors,"ISPRS J. Photogrammetry Remote Sensing, vol. 45, pp. 90-110, 1990.
[19] P. Meer, J.-M. Jolion, and A. Rosenfeld, "A fast parallel algorithm for blind estimation of noise variance,"IEEE Trans. Pattern Anal. Machine Intell., vol. 12, no. 2, pp. 216-223, Feb. 1990.
[20] S.K. Nayar, K. Ikeuchi, and T. Kanade, "Surface reflection: Physical and geometrical perspectives,"IEEE Trans. Pattern Anal. Machine Intell., vol. 13, no. 7, pp. 611-634, July 1991.
[21] C. Novak, S. Shafer, and R. Wilson, "Obtaining accurate color images for machine vision research," inProc. SPIE Conf. Perceiving, Measuring, and Using Color, Santa Clara, CA, Feb. 1990.
[22] A. V. Oppenheim and R. W. Schafer,Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1975.
[23] A. Papoulis,Probability and Statistics. Englewood Cliffs, NJ: Prentice, 1990.
[24] S.A. Shafer, "Using color to seperate reflection components,"COLOR Research and Application, vol. 10, no. 4, pp. 210-218, 1985.
[25] R.Y. Tsai, "A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses,"IEEE J. of Robotics and Automation, Vol. RA-3, No. 4, Aug. 1987, pp. 323-344.
[26] L. Wolff, "Polarization-based material classification from specular reflection,"IEEE Trans. Pattern Anal. Machine Intell., vol. 12, no. 11, pp. 1059-1071, 1990.

Index Terms:
calibration; video cameras; reflectivity; computer vision; CCD image sensors; edge detection; radiometry; parameter estimation; semiconductor device noise; semiconductor device models; radiometric CCD camera calibration; noise estimation; primary cue; visual processes; edge detection; shape from shading; video cameras; material reflectance; digitized pixel values; sensor noise; scene variation; camera characterization; scene description; fixed pattern nonuniformity; spatial variation; dark current
Citation:
G. Healey, R. Kondepudy, "Radiometric CCD camera calibration and noise estimation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 3, pp. 267-276, March 1994, doi:10.1109/34.276126
Usage of this product signifies your acceptance of the Terms of Use.