Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts

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dc.contributor.authorMerino, Ibon
dc.contributor.authorAzpiazu, Jon
dc.contributor.authorRemazeilles, Anthony
dc.contributor.authorSierra, Basilio
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionROBOTICA_FLEX
dc.contributor.institutionRobótica Médica
dc.date.issued2020-06-01
dc.descriptionPublisher Copyright: © 2020 by the authors.
dc.description.abstractThis article deals with the 2D image-based recognition of industrial parts. Methods based on histograms are well known and widely used, but it is hard to find the best combination of histograms, most distinctive for instance, for each situation and without a high user expertise. We proposed a descriptor subset selection technique that automatically selects the most appropriate descriptor combination, and that outperforms approach involving single descriptors. We have considered both backward and forward mechanisms. Furthermore, to recognize the industrial parts a supervised classification is used with the global descriptors as predictors. Several class approaches are compared. Given our application, the best results are obtained with the Support Vector Machine with a combination of descriptors increasing the F1 by 0.031 with respect to the best descriptor alone.en
dc.description.statusPeer reviewed
dc.format.extent1
dc.format.extent1436087
dc.identifier.citationMerino , I , Azpiazu , J , Remazeilles , A & Sierra , B 2020 , ' Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts ' , Applied Sciences , vol. 10 , no. 11 , 3701 , pp. 3701 . https://doi.org/10.3390/app10113701
dc.identifier.doi10.3390/app10113701
dc.identifier.issn2076-3417
dc.identifier.otherresearchoutputwizard: 11556/934
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85086082672&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofApplied Sciences
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsComputer vision
dc.subject.keywordsFeature descriptor
dc.subject.keywordsHistogram
dc.subject.keywordsFeature subset selection
dc.subject.keywordsIndustrial objects
dc.subject.keywordsComputer vision
dc.subject.keywordsFeature descriptor
dc.subject.keywordsHistogram
dc.subject.keywordsFeature subset selection
dc.subject.keywordsIndustrial objects
dc.subject.keywordsGeneral Materials Science
dc.subject.keywordsInstrumentation
dc.subject.keywordsGeneral Engineering
dc.subject.keywordsProcess Chemistry and Technology
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsFluid Flow and Transfer Processes
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/820689/EU/Seamless and safe human - centred robotic applications for novel collaborative workplaces/SHERLOCK
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/820689/EU/Seamless and safe human - centred robotic applications for novel collaborative workplaces/SHERLOCK
dc.subject.keywordsFunding Info
dc.subject.keywordsThis paper has been supported by the project SHERLOCK under the European Union’s Horizon 2020 Research & Innovation programme, grant agreement No. 820689.
dc.subject.keywordsThis paper has been supported by the project SHERLOCK under the European Union’s Horizon 2020 Research & Innovation programme, grant agreement No. 820689.
dc.titleHistogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Partsen
dc.typejournal article
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