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Proceedings Paper

Scaling analysis of thermographic images using neural networks
Author(s): Mark A. Johnson; Lawrence V. Meisel
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Paper Abstract

Sequences of thermographic images of burning residue produced by M198 155 (unicharge) test rounds fired at Yuma Proving Ground (YPG) have been collected for analysis with the objective being to elucidate the evolution of conditions in the breech after firing and to provide guidance in determining safe loading protocols for future autoloaders. In order to better understand the thermal environment in the breech, we are developing advanced analytical tools that can be used to quantitatively characterize sequences of thermographic images. However, the calibration data required to extract the temperature profiles of the YPG thermographic images for these analyses is unavailable. No analytic solution could be determined to perform the highly nonlinear reverse transformation from RGB space to intensities, therefore, a neural network has been employed. Furthermore, the experimental data provided by YPG is only measurable over a restricted range of temperatures extending from approximately 80 degree(s)C up to 110 degree(s)C. Since the highest temperatures measured in the thermographic data do not correspond to a hazardous condition, more complex measures than simple statistical averages of the temperature must be employed. A new numerical technique is introduced for measuring the scaling properties of single valued surfaces in 3-space represented by sparse data sets.

Paper Details

Date Published: 4 April 1997
PDF: 6 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271484
Show Author Affiliations
Mark A. Johnson, Benet Labs. (United States)
Lawrence V. Meisel, Benet Labs. (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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