Comparison of Neural Network and Markov Random Field Image Segmentation Techniques*

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dc.contributor.author Smith, Fred
dc.contributor.author Jepsen, Karen
dc.contributor.author Lichtenwalner, Peter
dc.date 2018-02-14T06:49:19.000
dc.date.accessioned 2020-06-30T06:39:52Z
dc.date.available 2020-06-30T06:39:52Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 1992
dc.date.issued 1992
dc.description.abstract <p>The interpretation of data from nondestructive evaluation (NDE) techniques is a tedious and time-consuming manual process that is subject to such random variables as scan quality, and inspector expertise and fatigue. The authors are researching methods to automatically recognize defects in ultrasonic images of aircraft structures. A typical wing skin image with an annotated defect is shown in Figure 1. Our ultimate goal is to reduce total fabrication time and improve inspection reliability.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/qnde/1992/allcontent/92/
dc.identifier.articleid 2981
dc.identifier.contextkey 5800909
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath qnde/1992/allcontent/92
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/60038
dc.language.iso en
dc.relation.ispartofseries Review of Progress in Quantitative Nondestructive Evaluation
dc.source.bitstream archive/lib.dr.iastate.edu/qnde/1992/allcontent/92/1992_Smith_ComparisonNeural.pdf|||Sat Jan 15 02:29:31 UTC 2022
dc.source.uri 10.1007/978-1-4615-3344-3_92
dc.title Comparison of Neural Network and Markov Random Field Image Segmentation Techniques*
dc.type event
dc.type.genre article
dspace.entity.type Publication
relation.isSeriesOfPublication 289a28b5-887e-4ddb-8c51-a88d07ebc3f3
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