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Faculty/Staff Name Title Institution Email
by
Clem Karl Co-pi Bu
,
David Castañón Co-pi Bu
,
Ahmet Tuysuzoglu
,
Phd Bu
,
Limor Martin
,
Phd Bu
BibTeX
@MISC{Bu_faculty/staffname,
author = {Clem Karl Co-pi Bu and David Castañón Co-pi Bu and Ahmet Tuysuzoglu and Phd Bu and Limor Martin and Phd Bu},
title = {Faculty/Staff Name Title Institution Email},
year = {}
}
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Abstract
a challenging problem is to correctly identify and label objects in a scene from X-ray projection data. The presence of artifacts in the reconstructed image confounds traditional segmentation and labeling methods. In this work, we focus on integrating machine learning, physical modeling and Bayesian inference in a framework for direct material identifi cation and labeling. Such an approach can mitigate image artifacts, reduce the number of corner cases and minimize false alarms.
