Measuring irregularities and surface defects from printed patterns
Vartiainen, Jarkko (2007-04-07)
Väitöskirja
Vartiainen, Jarkko
07.04.2007
Acta Universitatis LappeenrantaensisURN:ISSN:1456-4491
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-214-371-6
https://urn.fi/URN:ISBN:978-952-214-371-6
Tiivistelmä
Quality inspection and assurance is a veryimportant step when today's products are sold to markets. As products are produced in vast
quantities, the interest to automate quality inspection tasks has
increased correspondingly. Quality inspection tasks usuallyrequire
the detection of deficiencies, defined as irregularities in this
thesis.
Objects containing regular patterns appear quite frequently on
certain industries and science, e.g. half-tone raster patterns in the
printing industry, crystal lattice structures in solid state physics
and solder joints and components in the electronics industry. In this
thesis, the problem of regular patterns and irregularities is
described in analytical form and three different detection methods are
proposed. All the methods are based on characteristics of Fourier
transform to represent regular information compactly. Fourier
transform enables the separation of regular and irregular parts of an
image but the three methods presented are shown to differ in
generality and computational complexity.
Need to detect fine and sparse details is common in quality
inspection tasks, e.g., locating smallfractures in components in the
electronics industry or detecting tearing from paper samples in the
printing industry. In this thesis, a general definition of such
details is given by defining sufficient statistical properties in the
histogram domain. The analytical definition allowsa quantitative
comparison of methods designed for detail detection. Based on the
definition, the utilisation of existing thresholding methodsis shown
to be well motivated. Comparison of thresholding methods shows that
minimum error thresholding outperforms other standard
methods.
The results are successfully applied to a paper printability and
runnability inspection setup. Missing dots from a repeating raster
pattern are detected from Heliotest strips and small surface defects
from IGT picking papers.
quantities, the interest to automate quality inspection tasks has
increased correspondingly. Quality inspection tasks usuallyrequire
the detection of deficiencies, defined as irregularities in this
thesis.
Objects containing regular patterns appear quite frequently on
certain industries and science, e.g. half-tone raster patterns in the
printing industry, crystal lattice structures in solid state physics
and solder joints and components in the electronics industry. In this
thesis, the problem of regular patterns and irregularities is
described in analytical form and three different detection methods are
proposed. All the methods are based on characteristics of Fourier
transform to represent regular information compactly. Fourier
transform enables the separation of regular and irregular parts of an
image but the three methods presented are shown to differ in
generality and computational complexity.
Need to detect fine and sparse details is common in quality
inspection tasks, e.g., locating smallfractures in components in the
electronics industry or detecting tearing from paper samples in the
printing industry. In this thesis, a general definition of such
details is given by defining sufficient statistical properties in the
histogram domain. The analytical definition allowsa quantitative
comparison of methods designed for detail detection. Based on the
definition, the utilisation of existing thresholding methodsis shown
to be well motivated. Comparison of thresholding methods shows that
minimum error thresholding outperforms other standard
methods.
The results are successfully applied to a paper printability and
runnability inspection setup. Missing dots from a repeating raster
pattern are detected from Heliotest strips and small surface defects
from IGT picking papers.
Kokoelmat
- Väitöskirjat [1099]