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dc.contributor.authorAytaç, T.en_US
dc.contributor.authorBarshan, Billuren_US
dc.coverage.spatialIstanbul, Turkey
dc.date.accessioned2016-02-08T11:36:49Z
dc.date.available2016-02-08T11:36:49Z
dc.date.issued2008-10en_US
dc.identifier.urihttp://hdl.handle.net/11693/26820
dc.descriptionDate of Conference: 27-29 Oct. 2008
dc.descriptionConference name: 23rd International Symposium on Computer and Information Sciences, ISCIS 2008
dc.description.abstractThis study investigates the use of low-cost infrared (IR) sensors for the determination of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders using artificial neural networks (ANNs). The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way which cannot be represented by a simple analytical relationship, therefore complicating the localization and classification process. We propose the use of angular intensity scans and feature vectors obtained by modeling of angular intensity scans and present two different neural network based approaches in order to classify the geometry and/or the surface type of the targets. In the first case, where planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material are differentiated, an average correct classification rate of 78% of both geometry and surface over all target types is achieved. In the second case, where planes, 90° edges, and cylinders covered with different surface materials are differentiated, an average correct classification rate of 99.5% is achieved. The method demonstrated shows that ANNs can be used to extract substantially more information than IR sensors are commonly employed for. © 2008 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.title23rd International Symposium on Computer and Information Sciences, ISCIS 2008en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ISCIS.2008.4717907en_US
dc.subjectAluminaen_US
dc.subjectBackpropagationen_US
dc.subjectComputational geometryen_US
dc.subjectCylinders (shapes)en_US
dc.subjectGeometryen_US
dc.subjectInformation scienceen_US
dc.subjectInfrared detectorsen_US
dc.subjectPackaging materialsen_US
dc.subjectSensor networksen_US
dc.subjectSensorsen_US
dc.subjectSurface propertiesen_US
dc.subjectSurfacesen_US
dc.subjectTargetsen_US
dc.subjectTrace analysisen_US
dc.subjectClassification processesen_US
dc.subjectClassification ratesen_US
dc.subjectFeature vector (FV)en_US
dc.subjectInfrared (IR) sensorsen_US
dc.subjectIntensity measurementsen_US
dc.subjectStyrofoam packagingen_US
dc.subjectSurface materialsen_US
dc.subjectSurface typeen_US
dc.subjectTarget classificationen_US
dc.subjectNeural networksen_US
dc.titleTarget classification with simple infrared sensors using artificial neural networksen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage[1]
dc.citation.epage[6]
dc.identifier.doi10.1109/ISCIS.2008.4717907en_US
dc.publisherIEEE


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