Hyppää sisältöön
    • Suomeksi
    • På svenska
    • In English
  • Suomeksi
  • In English
  • Kirjaudu
Näytä aineisto 
  •   Etusivu
  • LUTPub
  • Väitöskirjat
  • Näytä aineisto
  •   Etusivu
  • LUTPub
  • Väitöskirjat
  • Näytä aineisto
JavaScript is disabled for your browser. Some features of this site may not work without it.

Reconstruction and analysis of surface variation using photometric stereo

Kuparinen, Toni (2008-12-12)

Katso/Avaa
isbn9789522146595.pdf (8.351Mb)
Lataukset: 


Väitöskirja

Kuparinen, Toni
12.12.2008
Lappeenranta University of Technology

Acta Universitatis Lappeenrantaensis

Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-214-6595

Tiivistelmä

In many industrial applications, accurate and fast surface reconstruction is essential for quality control. Variation in surface finishing parameters, such as surface roughness, can reflect defects in a manufacturing process, non-optimal product operational efficiency, and reduced life expectancy of the product. This thesis considers reconstruction and analysis of high-frequency variation, that is roughness, on planar surfaces. Standard roughness measures in industry are calculated from surface topography. A fast and non-contact method to obtain surface topography is to apply photometric stereo in the estimation of surface gradients and to reconstruct the surface by integrating the gradient fields. Alternatively, visual methods, such as statistical measures, fractal dimension and distance transforms, can be used to characterize surface roughness directly from gray-scale images. In this thesis, the accuracy of distance transforms, statistical measures, and fractal dimension are evaluated in the estimation of surface roughness from gray-scale images and topographies. The results are contrasted to standard industry roughness measures. In distance transforms, the key idea is that distance values calculated along a highly varying surface are greater than distances calculated along a smoother surface. Statistical measures and fractal dimension are common surface roughness measures. In the experiments, skewness and variance of brightness distribution, fractal dimension, and distance transforms exhibited strong linear correlations to standard industry roughness measures. One of the key strengths of photometric stereo method is the acquisition of higher frequency variation of surfaces. In this thesis, the reconstruction of planar high-frequency varying surfaces is studied in the presence of imaging noise and blur. Two Wiener filterbased methods are proposed of which one is optimal in the sense of surface power spectral density given the spectral properties of the imaging noise and blur. Experiments show that the proposed methods preserve the inherent high-frequency variation in the reconstructed surfaces, whereas traditional reconstruction methods typically handle incorrect measurements by smoothing, which dampens the high-frequency variation.
Kokoelmat
  • Väitöskirjat [1112]
LUT-yliopisto
PL 20
53851 Lappeenranta
Ota yhteyttä | Tietosuoja | Saavutettavuusseloste
 

 

Tämä kokoelma

JulkaisuajatTekijätNimekkeetKoulutusohjelmaAvainsanatSyöttöajatYhteisöt ja kokoelmat

Omat tiedot

Kirjaudu sisäänRekisteröidy
LUT-yliopisto
PL 20
53851 Lappeenranta
Ota yhteyttä | Tietosuoja | Saavutettavuusseloste