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Proceedings Paper

Automatic characterization of cross-sectional coated particle nuclear fuel using greedy coupled Bayesian snakes
Author(s): Jeffery R. Price; Deniz Aykac; John D. Hunn; Andrew K. Kercher
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Paper Abstract

We describe new image analysis developments in support of the U.S. Department of Energy's (DOE) Advanced Gas Reactor (AGR) Fuel Development and Qualification Program. We previously reported a non-iterative, Bayesian approach for locating the boundaries of different particle layers in cross-sectional imagery. That method, however, had to be initialized by manual preprocessing where a user must select two points in each image, one indicating the particle center and the other indicating the first layer interface. Here, we describe a technique designed to eliminate the manual preprocessing and provide full automation. With a low resolution image, we use "EdgeFlow" to approximate the layer boundaries with circular templates. Multiple snakes are initialized to these circles and deformed using a greedy Bayesian strategy that incorporates coupling terms as well as a priori information on the layer thicknesses and relative contrast. We show results indicating the effectiveness of the proposed method.

Paper Details

Date Published: 17 February 2007
PDF: 10 pages
Proc. SPIE 6503, Machine Vision Applications in Industrial Inspection XV, 650302 (17 February 2007); doi: 10.1117/12.702759
Show Author Affiliations
Jeffery R. Price, Oak Ridge National Lab. (United States)
Deniz Aykac, Oak Ridge National Lab. (United States)
John D. Hunn, Oak Ridge National Lab. (United States)
Andrew K. Kercher, Oak Ridge National Lab. (United States)

Published in SPIE Proceedings Vol. 6503:
Machine Vision Applications in Industrial Inspection XV
Fabrice Meriaudeau; Kurt S. Niel, Editor(s)

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