Target differentiation with simple infrared sensors using statistical pattern recognition techniques

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dc.citation.epage2620en_US
dc.citation.issueNumber10en_US
dc.citation.spage2607en_US
dc.citation.volumeNumber40en_US
dc.contributor.authorBarshan, B.en_US
dc.contributor.authorAytaç, T.en_US
dc.contributor.authorYüzbaşıoğlu, Ç.en_US
dc.date.accessioned2016-02-08T10:12:53Z
dc.date.available2016-02-08T10:12:53Z
dc.date.issued2007en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThis study compares the performances of various statistical pattern recognition techniques for the differentiation of commonly encountered features in indoor environments, possibly with different surface properties, using simple infrared (IR) sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the differentiation process. We construct feature vectors based on the parameters of angular IR intensity scans from different targets to determine their geometry and/or surface type. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. Parametric differentiation correctly identifies six different surface types of the same planar geometry, resulting in the best surface differentiation rate (100%). However, this rate is not maintained with the inclusion of more surfaces. The results indicate that the geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. The results demonstrate that simple IR sensors, when coupled with appropriate processing and recognition techniques, can be used to extract substantially more information than such devices are commonly employed for.en_US
dc.identifier.doi10.1016/j.patcog.2007.01.007en_US
dc.identifier.eissn1873-5142
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/11693/23365
dc.language.isoEnglishen_US
dc.publisherElsevier BVen_US
dc.relation.isversionofhttps://doi.org/10.1016/j.patcog.2007.01.007en_US
dc.source.titleTarget differentiationen_US
dc.subjectGeometry differentiationen_US
dc.subjectSurface differentiationen_US
dc.subjectStatistical pattern recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectInfrared sensorsen_US
dc.subjectOptical sensingen_US
dc.titleTarget differentiation with simple infrared sensors using statistical pattern recognition techniquesen_US
dc.typeArticleen_US

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