Paper
17 December 1993 Real-time defect detection using multiaperture fiber optic sensors and machine learning
Hendrik Rothe, Angela Duparre, Peter Riedel, Monika Timm
Author Affiliations +
Proceedings Volume 1989, Computer Vision for Industry; (1993) https://doi.org/10.1117/12.164860
Event: Electronic Imaging Device Engineering, 1993, Munich, Germany
Abstract
Smooth surfaces are widely applied in modern technology. Therefore a large variety of approaches for the determination of microtopographic surface descriptors has been developed. But there is a lack in adapted techniques for in-process surface assessment and defect detection. This paper deals with the development of an in-process sensor based on fiber optics and multivariate statistical signal processing.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hendrik Rothe, Angela Duparre, Peter Riedel, and Monika Timm "Real-time defect detection using multiaperture fiber optic sensors and machine learning", Proc. SPIE 1989, Computer Vision for Industry, (17 December 1993); https://doi.org/10.1117/12.164860
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Fiber optics sensors

Computer vision technology

Machine vision

Principal component analysis

Bidirectional reflectance transmission function

Defect detection

Glasses

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