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dc.contributor.authorCañero-Nieto, Juan Miguel
dc.contributor.authorSolano-Martos, Jose Francisco 
dc.contributor.authorMartín-Fernández, Francisco de Sales 
dc.date.accessioned2019-07-15T07:12:33Z
dc.date.available2019-07-15T07:12:33Z
dc.date.created2019
dc.date.issued2019-07-15
dc.identifier.urihttps://hdl.handle.net/10630/18045
dc.description.abstractThe present work is intended for residual oxide scale detection and classification through the application of image processing techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be compared and evaluated their performance..en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleA comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production linesen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroEscuela de Ingenierías Industrialesen_US
dc.relation.eventtitle8th Manufacturing Engineering Society International Conferenceen_US
dc.relation.eventplaceMadrid, Españaen_US
dc.relation.eventdate19/06/2019en_US
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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