@inproceedings{0cd18fef079f4dcbb038dcf16919e8cb,
title = "Automatic characterization of cross-sectional coated particle nuclear fuel using greedy coupled Bayesian snakes",
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.",
keywords = "Coated-particle nuclear fuel, Edgeflow, Image segmentation, Image-based metrology, Multiple active contours, Snakes, TRISO fuel",
author = "Price, {Jeffery R.} and Deniz Aykac and Hunn, {John D.} and Kercher, {Andrew K.}",
year = "2007",
doi = "10.1117/12.702759",
language = "English",
isbn = "0819466166",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
booktitle = "Proceedings of SPIE-IS and T Electronic Imaging - Machine Vision Applications in Industrial Inspection XV",
note = "Machine Vision Applications in Industrial Inspection XV ; Conference date: 29-01-2007 Through 30-01-2007",
}