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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Surface differentiation by parametric modeling of infrared intensity scans

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      Author
      Aytaç, T.
      Barshan, B.
      Date
      2005
      Source Title
      Optical Engineering
      Print ISSN
      0091-3286
      Electronic ISSN
      1560-2303
      Publisher
      SPIE-International Society for Optical Engineering
      Volume
      44
      Issue
      6
      Pages
      067202 (1-9)
      Language
      English
      Type
      Article
      Item Usage Stats
      102
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      96
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      Abstract
      We differentiate surfaces with different properties with simple low-cost IR emitters and detectors in a location-invariant manner. The intensity readings obtained with such sensors are highly dependent on the location and properties of the surface, which complicates the differentiation and localization process. Our approach, which models IR intensity scans parametrically, can distinguish different surfaces independent of their positions. Once the surface type is identified, its position (r, θ) can also be estimated. The method is verified experimentally with wood; Styrofoam packaging material; white painted matte wall; white and black cloth; and white, brown, and violet paper. A correct differentiation rate of 100% is achieved for six surfaces, and the surfaces are localized within absolute range and azimuth errors of 0.2 cm and 1.1 deg, respectively. The differentiation rate decreases to 86% for seven surfaces and to 73% for eight surfaces. The method demonstrated shows that simple IR sensors, when coupled with appropriate signal processing, can be used to recognize different types of surfaces in a location-invariant manner.
      Keywords
      Surface differentiation
      Infrared sensors
      Position estimation
      Lambertian reflection
      Phong model
      Pattern recognition
      Feature extraction
      Optical sensing
      Permalink
      http://hdl.handle.net/11693/24049
      Published Version (Please cite this version)
      https://doi.org/10.1117/1.1931467
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      • Department of Electrical and Electronics Engineering 3524
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