An Evolutionary Learning Approach to Self-configuring Image Pipelines in the Context of Carbon Fiber Fault Detection
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gp-bibliography.bib Revision:1.8081
- @InProceedings{Margraf:2017:ICMLA,
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author = "Andreas Margraf and Anthony Stein and
Leonhard Engstler and Steffen Geinitz and Joerg Haehner",
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booktitle = "2017 16th IEEE International Conference on Machine
Learning and Applications (ICMLA)",
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title = "An Evolutionary Learning Approach to Self-configuring
Image Pipelines in the Context of Carbon Fiber Fault
Detection",
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year = "2017",
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pages = "147--154",
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abstract = "Carbon fibre reinforced plastics (CFRP) play a key
role for the production of lightweight structures.
Simultaneously, online quality inspection of CFRP
becomes more important, especially for environments
with high safety standards. In this context, vision
systems aim to find defects of different shape, size,
contour and orientation. Little effort, however, has
been made in detecting defect areas in images taken
from the surface of carbon fibres. A common approach
for segmenting filament defects are edge detection and
thresholding. With every change of material and process
adjustments, the filter parameters have to be adapted.
In this paper, we propose a cartesian genetic
programming (CGP) approach to semi-automatically select
the best parameters. This strategy saves time for
parameter identification while at the same time
increases precision. A test run on randomly selected
samples shows how the approach can substantially
improve detection reliability.",
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keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming",
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DOI = "doi:10.1109/ICMLA.2017.0-165",
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month = dec,
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notes = "Also known as \cite{8260627}",
- }
Genetic Programming entries for
Andreas Margraf
Anthony Stein
Leonhard Engstler
Steffen Geinitz
Joerg Haehner
Citations