Introduction

  • Antonio Robles-Kelly
  • Cong Phuoc Huynh
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

In this chapter, we explore the opportunities, application areas and challenges involving the use of imaging spectroscopy as a means for scene understanding. This is important, since scene analysis in the scope of imaging spectroscopy involves the ability to robustly encode material properties, object composition and concentrations of primordial components in a scene. The combination of spatial and compositional information opens up a vast number of application possibilities. This combination of a broad domain of application with the use of key technologies makes the use of imaging spectroscopy a worthwhile opportunity for researchers in the areas of computer vision and pattern recognition.

Keywords

Biomass Chlorophyll Refraction Sorting Neon 

References

  1. Atkinson, G., & Hancock, E. R. (2005a). Multi-view surface reconstruction using polarization. In International conference on computer vision (pp. 309–316). Google Scholar
  2. Atkinson, G., & Hancock, E. R. (2005b). Recovery of surface height using polarization from two views. In CAIP (pp. 162–170). Google Scholar
  3. Drbohlav, O., & Sára, R. (2001). Unambigous determination of shape from photometric stereo with unknown light sources. In International conference on computer vision (pp. 581–586). Google Scholar
  4. Fu, Z., & Robles-Kelly, A. (2011a). Discriminant absorption feature learning for material classification. IEEE Transactions on Geoscience and Remote Sensing, 49(5), 1536–1556. CrossRefGoogle Scholar
  5. Fu, Z., & Robles-Kelly, A. (2011b). MILIS: multiple instance learning with instance selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 958–977. CrossRefGoogle Scholar
  6. Healey, G., & Slater, D. (1999). Invariant recognition in hyperspectral images. In IEEE conference on computer vision and pattern recognition (p. 1438). Google Scholar
  7. Huynh, C. P., & Robles-Kelly, A. (2010a). A solution of the dichromatic model for multispectral photometric invariance. International Journal of Computer Vision, 90(1), 1–27. CrossRefGoogle Scholar
  8. Huynh, C. P., & Robles-Kelly, A. (2010b). Hyperspectral imaging for skin recognition and biometrics. In International conference on image processing. Google Scholar
  9. Huynh, C. P., Robles-Kelly, A., & Hancock, E. R. (2010). Shape and refractive index recovery from single-view polarisation images. In IEEE conference on computer vision and pattern recognition. Google Scholar
  10. Rahmann, S., & Canterakis, N. (2001). Reconstruction of specular surfaces using polarization imaging. In IEEE conference on computer vision and pattern recognition (Vol. 1, 149–155). Google Scholar
  11. Raskar, R., Tumblin, J., Mohan, A., Agrawal, A., & Li, Y. (2006). Computational photography. In Proceeding of eurographics: state of the art report STAR. Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Antonio Robles-Kelly
    • 1
  • Cong Phuoc Huynh
    • 1
  1. 1.National ICT AustraliaCanberraAustralia

Personalised recommendations