A comparative analysis of different approaches to target differentiation and localization using infrared sensors (2006)
Abstract / truncated to 115 words
This study compares the performances of various techniques for the differentiation and localization of commonly encountered features in indoor environments, such as planes, corners, edges, and cylinders, possibly with different surface properties, using simple infrared 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 localization and differentiation process. The techniques considered include rule-based, template-based, and neural network-based target differentiation, parametric surface differentiation, and statistical pattern recognition techniques such as parametric density estimation, various linear and quadratic classifiers, mixture of normals, kernel estimator, k-nearest neighbor, artificial neural network, ... toggle 9 keywordsinfrared sensors – optical sensing – target differentiation – target localization – surface recognition – position estimation – feature extraction – statistical pattern recognition – artificial neural networks
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