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      İstatistiksel örüntü tanıma teknikleri kullanarak kızılberisi algılayıcılarla hedef ayırdetme

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      Author(s)
      Aytaç, Tayfun
      Yüzbaşıoǧlu, Çağrı
      Barshan, Billur
      Date
      2006-04
      Source Title
      2006 IEEE 14th Signal Processing and Communications Applications Conference
      Publisher
      IEEE
      Pages
      1 - 4
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      This 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.
      Keywords
      Gaussians classifiers
      Infrared sensors
      Parametric density estimation
      Parametric differentiation
      Statistical pattern recognition
      Image analysis
      Pattern recognition
      Probability distributions
      Statistical methods
      Temperature sensors
      Target tracking
      Permalink
      http://hdl.handle.net/11693/27148
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2006.1659804
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      • Department of Electrical and Electronics Engineering 3702
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