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dc.contributor.authorAyta̧ç, T.en_US
dc.contributor.authorBarshan, B.en_US
dc.date.accessioned2016-02-08T10:01:17Z
dc.date.available2016-02-08T10:01:17Z
dc.date.issued2009-01-30en_US
dc.identifier.issn0091-3286
dc.identifier.urihttp://hdl.handle.net/11693/22528
dc.description.abstractThis study investigates the use of simple, low-cost infrared sensors for the recognition of geometry and surface type of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We employ artificial neural networks to determine the geometry and the surface type of targets and provide experimental verification with three different geometries and three different surface types. The networks are trained with the Levenberg-Marquardt algorithm and pruned with the optimal brain surgeon technique. The geometry and the surface type of targets can be correctly classified with rates of 99 and 78.4%, respectively. An average correct classification rate of 78% is achieved when both geometry and surface type are differentiated. This indicates that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor in recognizing the patterns. The results demonstrate that processing the data from simple infrared sensors through suitable techniques can help us exploit their full potential and extend their usage beyond well-known applications.en_US
dc.language.isoEnglishen_US
dc.source.titleOptical Engineeringen_US
dc.relation.isversionofhttp://dx.doi.org/10.1117/1.3067874en_US
dc.subjectArtificial neural networksen_US
dc.subjectOptimal brain surgeonen_US
dc.subjectPattern recognitionen_US
dc.subjectSimple infrared detectorsen_US
dc.subjectTarget classificationen_US
dc.subjectTarget differentiationen_US
dc.titleRecognizing targets from infrared intensity scan patterns using artificial neural networksen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage017203-1en_US
dc.citation.epage017203-13en_US
dc.citation.volumeNumber48en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1117/1.3067874en_US
dc.publisherS P I E - International Society for Optical Engineeringen_US


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