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dc.contributor.authorAytaç, Tayfunen_US
dc.contributor.authorYüzbaşıoǧlu, Çağrıen_US
dc.contributor.authorBarshan, Billuren_US
dc.coverage.spatialAntalya, Turkey
dc.date.accessioned2016-02-08T11:45:53Z
dc.date.available2016-02-08T11:45:53Z
dc.date.issued2006-04en_US
dc.identifier.urihttp://hdl.handle.net/11693/27148
dc.descriptionDate of Conference: 17-19 April 2006
dc.descriptionConference name: 2006 IEEE 14th Signal Processing and Communications Applications
dc.description.abstractThis study compares the performances of different statistical pattern recognition techniques to differentiation of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders, using low-cost infrared sensors. The pattern recognition techniques compared include parametric density estimation, mixture of Gaussians, kernel estimator, k-nearest neighbor classifier, neural network classifier, and support vector machine classifier. A correct differentiation rate of 100% is achieved for six surfaces using parametric differentiation. For three geometries covered with seven different surfaces, best correct differentiation rate (100%) is achieved with mixture of Gaussians classifier with three components. The results demonstrate that simple infrared sensors, when coupled with appropriate processing, can be used to extract substantially more information than such devices are commonly employed. © 2006 IEEE.en_US
dc.language.isoTurkishen_US
dc.source.title2006 IEEE 14th Signal Processing and Communications Applications Conferenceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2006.1659804en_US
dc.subjectGaussians classifiersen_US
dc.subjectInfrared sensorsen_US
dc.subjectParametric density estimationen_US
dc.subjectParametric differentiationen_US
dc.subjectStatistical pattern recognitionen_US
dc.subjectImage analysisen_US
dc.subjectPattern recognitionen_US
dc.subjectProbability distributionsen_US
dc.subjectStatistical methodsen_US
dc.subjectTemperature sensorsen_US
dc.subjectTarget trackingen_US
dc.titleİstatistiksel örüntü tanıma teknikleri kullanarak kızılberisi algılayıcılarla hedef ayırdetmeen_US
dc.title.alternativeTarget differentiation with infrared sensors using statistical pattern recognition techniquesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage[1]
dc.citation.epage[4]
dc.identifier.doi10.1109/SIU.2006.1659804en_US
dc.publisherIEEE


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