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<p>Scipediacontent moved page <a href="/public/Draft_Content_765993575" class="mw-redirect" title="Draft Content 765993575">Draft Content 765993575</a> to <a href="/public/Geinitz_et_al_2017a" title="Geinitz et al 2017a">Geinitz et al 2017a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 18:23, 1 February 2021</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

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<p>Created page with &quot; == Abstract == Carbon fiber reinforced plastics (CFRP) play a key role for the production of leightweight structures. Simultaneously, online quality inspection of CFRP becom...&quot;</p> <p><b>New page</b></p><div><br /> == Abstract ==<br /> <br /> Carbon fiber reinforced plastics (CFRP) play a key role for the production of leightweight structures. Simultaneously, online quality inspection of CFRP becomes more important, especially for environments with high safety standards. In this context, vision systems aim to find defects of different shape, size, contour and orientation. Little effort, however, has been made in detecting defect areas in images taken from the surface of carbon fibers. A common approach for segmenting filament defects are edge detection and thresholding. With every change of material and process adjustments, the filter parameters have to be adapted. In this paper, we propose a cartesian genetic programming (CGP) approach to semi-automatically select the best parameters. This strategy saves time for parameter identification while at the same time increases precision. A test run on randomly selected samples shows how the approach can substantially improve detection reliability.<br /> <br /> <br /> == Original document ==<br /> <br /> The different versions of the original document can be found in:<br /> <br /> * [http://publica.fraunhofer.de/documents/N-520513.html http://publica.fraunhofer.de/documents/N-520513.html]<br /> <br /> * [http://xplorestaging.ieee.org/ielx7/8258911/8260597/08260627.pdf?arnumber=8260627 http://xplorestaging.ieee.org/ielx7/8258911/8260597/08260627.pdf?arnumber=8260627],<br /> : [http://dx.doi.org/10.1109/icmla.2017.0-165 http://dx.doi.org/10.1109/icmla.2017.0-165]<br /> <br /> * [https://dblp.uni-trier.de/db/conf/icmla/icmla2017.html#MargrafSEGH17 https://dblp.uni-trier.de/db/conf/icmla/icmla2017.html#MargrafSEGH17],<br /> : [https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/46812 https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/46812],<br /> : [https://academic.microsoft.com/#/detail/2783140365 https://academic.microsoft.com/#/detail/2783140365]<br /> <br /> * [https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/46812 https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/46812],<br /> : [https://doi.org/10.1109/icmla.2017.0-165 https://doi.org/10.1109/icmla.2017.0-165]</div>

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