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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Recognizing targets from infrared intensity scan patterns using artificial neural networks

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      Author
      Ayta̧ç, T.
      Barshan, B.
      Date
      2009-01-30
      Source Title
      Optical Engineering
      Print ISSN
      0091-3286
      Publisher
      S P I E - International Society for Optical Engineering
      Volume
      48
      Issue
      1
      Pages
      017203-1 - 017203-13
      Language
      English
      Type
      Article
      Item Usage Stats
      115
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      100
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      Abstract
      This study investigates the use of simple, low-cost infrared sensors for the recognition of geometry and surface type of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We employ artificial neural networks to determine the geometry and the surface type of targets and provide experimental verification with three different geometries and three different surface types. The networks are trained with the Levenberg-Marquardt algorithm and pruned with the optimal brain surgeon technique. The geometry and the surface type of targets can be correctly classified with rates of 99 and 78.4%, respectively. An average correct classification rate of 78% is achieved when both geometry and surface type are differentiated. This indicates that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor in recognizing the patterns. The results demonstrate that processing the data from simple infrared sensors through suitable techniques can help us exploit their full potential and extend their usage beyond well-known applications.
      Keywords
      Artificial neural networks
      Optimal brain surgeon
      Pattern recognition
      Simple infrared detectors
      Target classification
      Target differentiation
      Permalink
      http://hdl.handle.net/11693/22528
      Published Version (Please cite this version)
      http://dx.doi.org/10.1117/1.3067874
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      • Department of Electrical and Electronics Engineering 3524
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