İstatistiksel örüntü tanıma teknikleri kullanarak kızılberisi algılayıcılarla hedef ayırdetme
Date
2006-04Source Title
2006 IEEE 14th Signal Processing and Communications Applications Conference
Publisher
IEEE
Pages
1 - 4
Language
Turkish
Type
Conference PaperItem Usage Stats
152
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90
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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 classifiersInfrared sensors
Parametric density estimation
Parametric differentiation
Statistical pattern recognition
Image analysis
Pattern recognition
Probability distributions
Statistical methods
Temperature sensors
Target tracking