Aytaç, TayfunYüzbaşıoǧlu, ÇağrıBarshan, Billur2016-02-082016-02-082006-04http://hdl.handle.net/11693/27148Date of Conference: 17-19 April 2006Conference name: 2006 IEEE 14th Signal Processing and Communications ApplicationsThis 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.TurkishGaussians classifiersInfrared sensorsParametric density estimationParametric differentiationStatistical pattern recognitionImage analysisPattern recognitionProbability distributionsStatistical methodsTemperature sensorsTarget trackingConference Paper10.1109/SIU.2006.1659804