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      • Department of Electrical and Electronics Engineering
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      Target classification with simple infrared sensors using artificial neural networks

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      Author(s)
      Aytaç, T.
      Barshan, Billur
      Date
      2008-10
      Source Title
      23rd International Symposium on Computer and Information Sciences, ISCIS 2008
      Publisher
      IEEE
      Pages
      1 - 6
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
      191
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      Abstract
      This study investigates the use of low-cost infrared (IR) sensors for the determination of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders using artificial neural networks (ANNs). The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way which cannot be represented by a simple analytical relationship, therefore complicating the localization and classification process. We propose the use of angular intensity scans and feature vectors obtained by modeling of angular intensity scans and present two different neural network based approaches in order to classify the geometry and/or the surface type of the targets. In the first case, where planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material are differentiated, an average correct classification rate of 78% of both geometry and surface over all target types is achieved. In the second case, where planes, 90° edges, and cylinders covered with different surface materials are differentiated, an average correct classification rate of 99.5% is achieved. The method demonstrated shows that ANNs can be used to extract substantially more information than IR sensors are commonly employed for. © 2008 IEEE.
      Keywords
      Alumina
      Backpropagation
      Computational geometry
      Cylinders (shapes)
      Geometry
      Information science
      Infrared detectors
      Packaging materials
      Sensor networks
      Sensors
      Surface properties
      Surfaces
      Targets
      Trace analysis
      Classification processes
      Classification rates
      Feature vector (FV)
      Infrared (IR) sensors
      Intensity measurements
      Styrofoam packaging
      Surface materials
      Surface type
      Target classification
      Neural networks
      Permalink
      http://hdl.handle.net/11693/26820
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/ISCIS.2008.4717907
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