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Analytical and Bioanalytical Chemistry

, Volume 382, Issue 4, pp 868–872 | Cite as

Hyperspectral integrated computational imaging

  • Lisa A Cassis
  • Aaron Urbas
  • Robert A Lodder
Trends

In the past, optics has served mainly to render the world more easily visible to humans. Now, computers are increasingly employed to make sense of the visual world in ways that people cannot. With a new generation of optics, scientists and engineers are recasting visual scenes for interpretation exclusively by computers. To the human eye, these pictures appear distorted at best, or at worst look like visual noise, without discernable meaning. But to computers, such data are worth more than a thousand words. Optimizing complete vision-and-action systems for computers lies at the core of integrated computational imaging. Computers are well-established manipulators of digitized images, and image-processing programs do it routinely on desktop machines. However, what is new is the strategy of modifying image information as it is sensed to make it better suited for the “computer mind” [1, 2].

For example, rather than the customary concave and convex disks, optical engineers are fabricating...

Keywords

Hyperspectral Imaging Quantum Cascade Laser Transition Edge Sensor Hyperspectral Data Defense Advance Research Project Agency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research was funded in part by KSEF-333-RDE-003, the NIH through HL58927, the NSF through DGE-9870691, and NIH and SESI through SC-NIAAA-93-01. The research is being carried out in part at the National Synchrotron Light Source, Brookhaven National Laboratory, which is supported by the US Department of Energy, Division of Materials Sciences and Division of Chemical Sciences, under Contract No. DE-AC02-98CH10886.

References

  1. 1.
    Weiss P (2003) New lenses create distorted images for digital enhancement. Sci News 163(13):200Google Scholar
  2. 2.
    Cassis LA, Dai B, Urbas A, Lodder RA (2004) In vivo applications of a molecular computing-based high-throughput NIR spectrometer. Prog Biomed Opt Imaging (in press)Google Scholar
  3. 3.
  4. 4.
  5. 5.
    Bains S (2004) Wavefront coding finds increasing use. Laser Focus World 40(1). http://lfw.pennnet.com/
  6. 6.
    Terrestrial Planet Finder Book, NASA (1999) http://planetquest.jpl.nasa.gov/TPF/tpf_book/index.html
  7. 7.
    Lewis MF, Wilson RA (1994) The use of lenslet arrays in spatial light modulators. Pure Appl Opt 3:143–150CrossRefGoogle Scholar
  8. 8.
    Barge M, Hamam H, Defosse Y, Chevallier R, de Bougrenet de la Tocnaye JL (1996) Array illuminators based on diffractive optical elements. J Opt 27:151–170CrossRefGoogle Scholar
  9. 9.
    Liu Y, Windham WR, Lawrence KC, Park B (2003) Simple algorithms for the classification of visible/near-infrared and hyperspectral imaging spectra of chicken skins, feces, and fecal contaminated skins. Appl Spectrosc 57(12):1609–1612CrossRefGoogle Scholar
  10. 10.
    Gillies R, Freeman JE, Cancio LC, Brand D, Hopmeier M, Mansfield JR (2003) Systemic effects of shock and resuscitation monitored by visible hyperspectral imaging. Diab Technol Ther 5(5):847–855CrossRefGoogle Scholar
  11. 11.
    Gmachl C, Capasso F, et al. (2002) Ultra-broadband semiconductor laser. Nature 415:883–887CrossRefGoogle Scholar
  12. 12.
    Cabrera B, Clarke RM, Colling P, Miller AJ, Nam S, Romani RW (1998) Detection of single infrared, optical, and ultraviolet photons using superconducting transition edge sensors. Appl Phys Lett 73(6):735–737CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of KentuckyLexingtonUSA
  2. 2.Graduate Center for Nutritional SciencesUniversity of KentuckyLexingtonUSA

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