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An optimisation approach to the recovery of reflection parameters from a single hyperspectral image

journal contribution
posted on 2013-12-01, 00:00 authored by Sejuti Rahman, Antonio Robles-KellyAntonio Robles-Kelly
In this paper, we present a method to recover the parameters governing the reflection of light from a surface making use of a single hyperspectral image. To do this, we view the image radiance as a combination of specular and diffuse reflection components and present a cost functional which can be used for purposes of iterative least squares optimisation. This optimisation process is quite general in nature and can be applied to a number of reflectance models widely used in the computer vision and graphics communities. We elaborate on the use of these models in our optimisation process and provide a variant of the Beckmann–Kirchhoff model which incorporates the Fresnel reflection term. We show results on synthetic images and illustrate how the recovered photometric parameters can be employed for skin recognition in real world imagery, where our estimated albedo yields a classification rate of 95.09 ± 4.26% as compared to an alternative, whose classification rate is of 90.94 ± 6.12%. We also show quantitative results on the estimation of the index of refraction, where our method delivers an average per-pixel angular error of 0.15°. This is a considerable improvement with respect to an alternative, which yields an error of 9.9°.

History

Journal

Computer vision and image understanding

Volume

117

Pagination

1672-1688

Location

Amsterdam, The Netherlands

ISSN

1077-3142

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2013, Elsevier Inc.

Issue

12

Publisher

Elsevier

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