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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Machine Vision Applications in Industrial Inspection XV
PublisherSPIE
ISBN (Print)0819466166, 9780819466167
DOIs
StatePublished - 2007
EventMachine Vision Applications in Industrial Inspection XV - San Jose, CA, United States
Duration: Jan 29 2007Jan 30 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6503
ISSN (Print)0277-786X

Conference

ConferenceMachine Vision Applications in Industrial Inspection XV
Country/TerritoryUnited States
CitySan Jose, CA
Period01/29/0701/30/07

Keywords

  • Coated-particle nuclear fuel
  • Edgeflow
  • Image segmentation
  • Image-based metrology
  • Multiple active contours
  • Snakes
  • TRISO fuel

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