Spectral reflectance curves for multispectral imaging, combining different techniques and a neural network

Authors

  • C.A. Osorio-Gómez
  • E. Mejía-Ospino
  • J.E. Guerrero-Bermúdez.

Keywords:

Artificial neural network, spectral reflectance curves, multispectral imaging, curve fitting

Abstract

In this paper, we present an alternative procedure for the digital reconstruction of spectral reflectance curves of oil painting on canvas using multispectral imaging. The technique is based on a combination of the results obtained by pseudo-inverse, principal component analysis and interpolation; these results are the input to a feed-forward back propagation neural network fitting the values of the curves to a target obtained using a spectrophotometer Shimadzu UV2401. Goodness-of-Fit Coefficient (GFC), absolute mean error (ABE) and spectral Root Mean Squared error (RMS) are the metrics used to evaluate the performance of the procedure proposed.

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Published

2009-01-01

How to Cite

[1]
C. Osorio-Gómez, E. Mejía-Ospino, and J.E. Guerrero-Bermúdez., “Spectral reflectance curves for multispectral imaging, combining different techniques and a neural network”, Rev. Mex. Fís., vol. 55, no. 2, pp. 120–0, Jan. 2009.

Issue

Vol. 55 No. 2 (2009): Revista Mexicana de Física.

Section

Articles

License

Authors retain copyright and grant the Revista Mexicana de Física right of first publication with the work simultaneously licensed under a CC BY-NC-ND 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.