Automatic characterization of cross-sectional coated particle nuclear fuel using greedy coupled Bayesian snakes

Jeffery R. Price; Deniz Aykac; John D. Hunn; Andrew K. Kercher

doi:10.1117/12.702759

17 February 2007 Automatic characterization of cross-sectional coated particle nuclear fuel using greedy coupled Bayesian snakes

Jeffery R. Price, Deniz Aykac, John D. Hunn, Andrew K. Kercher

Author Affiliations +

Proceedings Volume 6503, Machine Vision Applications in Industrial Inspection XV; 650302 (2007) https://doi.org/10.1117/12.702759
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

Jeffery R. Price, Deniz Aykac, John D. Hunn, and Andrew K. Kercher "Automatic characterization of cross-sectional coated particle nuclear fuel using greedy coupled Bayesian snakes", Proc. SPIE 6503, Machine Vision Applications in Industrial Inspection XV, 650302 (17 February 2007); https://doi.org/10.1117/12.702759

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