Detection of irregularities in regular patterns

  • Authors:

  • Jarkko Vartiainen;Albert Sadovnikov;Joni-Kristian Kamarainen;Lasse Lensu;Heikki Kälviäinen

  • Affiliations:

  • Lappeenranta University of Technology, Machine Vision and Pattern Recognition Research Group, P.O. Box 20, 53851, Lappeenranta, Finland;Lappeenranta University of Technology, Machine Vision and Pattern Recognition Research Group, P.O. Box 20, 53851, Lappeenranta, Finland;Lappeenranta University of Technology, Machine Vision and Pattern Recognition Research Group, P.O. Box 20, 53851, Lappeenranta, Finland;Lappeenranta University of Technology, Machine Vision and Pattern Recognition Research Group, P.O. Box 20, 53851, Lappeenranta, Finland;Lappeenranta University of Technology, Machine Vision and Pattern Recognition Research Group, P.O. Box 20, 53851, Lappeenranta, Finland

  • Venue:
  • Machine Vision and Applications
  • Year:
  • 2008

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Abstract

Regular patterns, as defined in this study, are found in areas of industry and science, for example, halftone raster patterns used in the printing industry and crystal lattice structures in solid state physics. The need for quality inspection of products containing regular patterns has aroused interest in the application of machine vision for automatic inspection. Quality inspection typically corresponds to detecting abnormalities, defined as irregularities in this case. In this study, the problem of irregularity detection is described in analytical form and three different detection methods are proposed. All the methods are based on characteristics of the Fourier transform to compactly represent regular information. The Fourier transform enables the separation of regular and irregular parts of an input image. The three methods presented are shown to differ in their generality and computational complexities.