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Proceedings Paper

Characterization of surfaces using neural pattern recognition methods based on BRDF and AFM measurements
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Paper Abstract

A major problem of in-situ surface characterization by using angle-resolved light scattering is the fast and accurate surface parameter identification. This paper will deal with surface parameter identification methods from BRDF measurements of rough surfaces with stochastical height topographies. First, neural classification methods will be discussed. Second, the discussed classification method will be applied to BRDF data taken by an ARS silicon sensor with 8013 polar photodiodes. The classification results will be compared to topography data taken from AFM measurements. Finally, neural self-organized networks will be applied to classify in unsupervised manner rough surfaces based on BRDF measurements.

Paper Details

Date Published: 25 October 1999
PDF: 15 pages
Proc. SPIE 3784, Rough Surface Scattering and Contamination, (25 October 1999); doi: 10.1117/12.366693
Show Author Affiliations
Thomas Rinder, Univ. of the Federal Armed Forces (Germany)
Hendrik Rothe, Univ. of the Federal Armed Forces (Germany)


Published in SPIE Proceedings Vol. 3784:
Rough Surface Scattering and Contamination
Zu-Han Gu; Philip T. C. Chen; Zu-Han Gu; Alexei A. Maradudin; Alexei A. Maradudin, Editor(s)

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