Geometric calibration of a hyperspectral imaging system.

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 (1.0-1.7microm). The proposed method can accurately characterize wavelength dependent optical aberrations and produce transformations for efficient subpixel geometric calibration.

[1]  Vitold E. Pozhar,et al.  A spectral distortion correction method for an imaging spectrometer , 2009 .

[2]  C. Gendrin,et al.  Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review. , 2008, Journal of pharmaceutical and biomedical analysis.

[3]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[4]  David P. Bannon,et al.  Hyperspectral imaging: Cubes and slices , 2009 .

[5]  Tuan Vo-Dinh,et al.  Hyperspectral imaging system using acousto‐optic tunable filter for flow cytometry applications , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[6]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[7]  Carl Tim Kelley,et al.  Iterative methods for optimization , 1999, Frontiers in applied mathematics.

[8]  A. Siddiqi,et al.  Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells , 2008, Cancer.

[9]  Paul F. Whelan,et al.  Calibration and removal of lateral chromatic aberration in images , 2007, Pattern Recognit. Lett..

[10]  Reg G. Willson.  Modeling and calibration of automated zoom lenses , 1994, Other Conferences.

[11]  Miran Bürmen,et al.  Geometrical calibration of an AOTF hyper-spectral imaging system , 2010, BiOS.

[12]  Walter J. Riker.  A Review of J , 2010 .

[13]  Michael Unser,et al.  Splines: a perfect fit for signal and image processing , 1999, IEEE Signal Process. Mag..

[14]  Richard Kowarschik,et al.  Image aberrations in optical three-dimensional measurement systems with fringe projection. , 2004, Applied optics.

[15]  Carlos Ricolfe-Viala,et al.  Robust metric calibration of non-linear camera lens distortion , 2010, Pattern Recognit..

[16]  Colm P. O'Donnell,et al.  Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .

[17]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  R. Weissleder,et al.  Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging , 2002, European Radiology.

[20]  Duane C. Brown,et al.  Close-Range Camera Calibration , 1971 .

[21]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[22]  Miran Bürmen,et al.  Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems , 2010 .

[23]  F. Bookstein.  Thin-plate splines and decomposition of deformation , 1989 .