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Proceedings Paper

Neural networks for calibration tomography
Author(s): Arthur J. Decker
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Paper Abstract

Artificial neural networks are suitable for performing pattern-to-pattern calibrations. These calibrations are potentially useful for facilities operations in aeronautics, the control of optical alignment, and the like. This paper compares computed tomography with neural net calibration tomography for estimating density from its x-ray transform. X-ray transforms are measured, for example, in diffuse-illumination, holographic interferometry of fluids. Computed tomography and neural net calibration tomography are shown to have comparable performance for a 10 degree viewing cone and 29 interferograms within that cone. The system of tomography discussed is proposed as a relevant test of neural networks and other parallel processors intended for using flow visualization data.

Paper Details

Date Published: 2 December 1993
PDF: 8 pages
Proc. SPIE 2005, Optical Diagnostics in Fluid and Thermal Flow, (2 December 1993); doi: 10.1117/12.163741
Show Author Affiliations
Arthur J. Decker, NASA Lewis Research Ctr. (United States)

Published in SPIE Proceedings Vol. 2005:
Optical Diagnostics in Fluid and Thermal Flow
Soyoung Stephen Cha; James D. Trolinger, Editor(s)

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