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  4. Optical feature extraction with illumination-encoded linear functions
 
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2012
  • Konferenzbeitrag

Titel

Optical feature extraction with illumination-encoded linear functions

Abstract
The choice of an appropriate illumination design is one of the most important steps in creating successful machine vision systems for automated inspection tasks. In a popular technique, multiple inspection images are captured under angular-varying illumination directions over the hemisphere, which yields a set of images referred to as illumination series. However, most existing approaches are restricted in that they use rather simple patterns like point- or sector-shaped illumination patterns on the hemisphere. In this paper, we present an illumination technique which reduces the effort for capturing inspection images for each reflectance feature by using linear combinations of basis light patterns over the hemisphere as feature-specific illumination patterns. The key idea is to encode linear functions for feature extraction as angular-dependent illumination patterns, and thereby to compute linear features from the scene's reflectance field directly in the optical domain. In the experimental part, we evaluate the proposed illumination technique on the problem of optical material type classification of printed circuit boards (PCBs).
Author(s)
Gruna, Robin
Beyerer, Jürgen
Hauptwerk
Image processing: Machine vision applications V
Konferenz
Conference "Image Processing - Machine Vision Applications" 2012
DOI
10.1117/12.907409
File(s)
001.pdf (4.27 MB)
Language
Englisch
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Tags
  • KCM

  • optical feature extra...

  • hemispherical illumin...

  • reflectance fields

  • illumination series

  • material classificati...

  • multivariate image an...

  • automated visual insp...

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