Neural networks for calibration tomography

Arthur J. Decker

doi:10.1117/12.163741

2 December 1993 Neural networks for calibration tomography

Arthur J. Decker

Proceedings Volume 2005, Optical Diagnostics in Fluid and Thermal Flow; (1993) https://doi.org/10.1117/12.163741
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

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.

Citation Download Citation

Arthur J. Decker "Neural networks for calibration tomography", Proc. SPIE 2005, Optical Diagnostics in Fluid and Thermal Flow, (2 December 1993); https://doi.org/10.1117/12.163741

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