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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 15 — May. 20, 2010
  • pp: 2813–2818

Geometric calibration of a hyperspectral imaging system

Žiga Špiclin, Jaka Katrašnik, Miran Bürmen, Franjo Pernuš, and Boštjan Likar  »View Author Affiliations

Applied Optics, Vol. 49, Issue 15, pp. 2813-2818 (2010)

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Every imaging system requires a geometric calibration to yield accurate optical measurements. Geometric calibration typically involves imaging of a known calibration object and finding the parameters of a camera model and a model of optical aberrations. Optical aberrations can vary significantly across the wide spectral ranges of hyperspectral imaging systems, which can lead to inaccurate geometric calibrations if conventional methods were used. We propose a method based on a B-spline transformation field to align the spectral images of the calibration object to the model image of the calibration object. The degree of spatial alignment between the ideal and the spectral images is measured by normalized cross correlation. Geometric calibration was performed on a hyperspectral imaging system based on an acousto-optic tunable filter designed for the near-infrared spectral range ( 1.0 1.7 μm ). The proposed method can accurately characterize wavelength dependent optical aberrations and produce transformations for efficient subpixel geometric calibration.

© 2010 Optical Society of America

OCIS Codes
(080.1010) Geometric optics : Aberrations (global)
(100.2980) Image processing : Image enhancement
(150.1488) Machine vision : Calibration
(110.4234) Imaging systems : Multispectral and hyperspectral imaging

ToC Category:
Imaging Systems

Original Manuscript: February 17, 2010
Revised Manuscript: April 21, 2010
Manuscript Accepted: April 23, 2010
Published: May 13, 2010

Žiga Špiclin, Jaka Katrašnik, Miran Bürmen, Franjo Pernuš, and Boštjan Likar, "Geometric calibration of a hyperspectral imaging system," Appl. Opt. 49, 2813-2818 (2010)

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  1. D. Bannon, “Hyperspectral imaging: cubes and slices,” Nat. Photonics 3, 627–629 (2009). [CrossRef]
  2. C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review,” J. Pharm. Biomed. Anal. 48, 533–553 (2008). [CrossRef] [PubMed]
  3. A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228, 1147–1153 (1985). [CrossRef] [PubMed]
  4. A. Gowen, C. O’Donnell, P. Cullen, G. Downey, and J. Frias, “Hyperspectral imaging—an emerging process analytical tool for food quality and safety control,” Trends Food Sci. Technol. 18, 590–598 (2007). [CrossRef]
  5. P. Kasili and T. Vo-Dinh, “Hyperspectral imaging system using acousto-optic tunable filter for flow cytometry applications,” Cytometry Part A 69, 835–841 (2006). [CrossRef]
  6. A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008). [CrossRef]
  7. V. Ntziachristos, C. Bremer, and R. Weissleder, “Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging,” Eur. Radiol. 13, 195–208 (2003). [PubMed]
  8. J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992). [CrossRef]
  9. D. C. Brown, “Close-range camera calibration,” Photogramm. Eng. 37, 855–866 (1971).
  10. J. Mallon and P. F. Whelan, “Calibration and removal of lateral chromatic aberration in images,” Pattern Recogn. Lett. 28, 125–135 (2007). [CrossRef]
  11. P. Brakhage, G. Notni, and R. Kowarschik, “Image aberrations in optical three-dimensional measurement systems with fringe projection,” Appl. Opt. 43, 3217–3223(2004). [CrossRef] [PubMed]
  12. C. Ricolfe-Viala and A. Sánchez-Salmerón, “Robust metric calibration of non-linear camera lens distortion,” Pattern Recogn. 43, 1688–1699 (2010). [CrossRef]
  13. Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometrical calibration of an AOTF hyper-spectral imaging system,” Proc. SPIE 7556, 75560I (2010). [CrossRef]
  14. A. Machihin and V. Pozhar, “A spectral distortion correction method for an imaging spectrometer,” Instrum. Exp. Tech. 52, 847–853 (2009). [CrossRef]
  15. M. Unser, “Splines—a perfect fit for signal and image processing,” IEEE Signal Process Mag. 16, 22–38 (1999). [CrossRef]
  16. D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999). [CrossRef] [PubMed]
  17. F. L. Bookstein, “Principal warps: thin-plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989). [CrossRef]
  18. C. T. Kelley, Iterative Methods for Optimization (Society for Industrial Mathematics, 1999). [CrossRef]
  19. J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemom. Intell. Lab. Syst. 101, 23–29 (2010). [CrossRef]
  20. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the Fourth Alvey Vision Conference (University of Manchester, 1988), pp. 147–152.
  21. R. Willson, “Modeling and calibration of automated zoom lenses,” Ph.D. dissertation (Carnegie Mellon, 1994).

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