Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Optical feature extraction with illumination-encoded linear functions

: Gruna, Robin; Beyerer, Jürgen

Postprint urn:nbn:de:0011-n-2018749 (4.2 MByte PDF)
MD5 Fingerprint: 898cb21a0f23837f68c39eb92ad0f41d
Copyright 2012 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 26.4.2012

Bingham, P. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Image processing: Machine vision applications V : SPIE Electronic Imaging 2012; 25 January 2012, Burlingame, California, USA
Bellingham, WA: SPIE, 2012 (Proceedings of SPIE 8300)
ISBN: 978-0-8194-8947-0
Paper 830004
Conference "Image Processing - Machine Vision Applications" <5, 2012, Burlingame/Calif.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
KCM; optical feature extraction; hemispherical illumination functions; reflectance fields; illumination series; material classification; multivariate image analysis; automated visual inspection

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).