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Thresholding-based detection of fine and sparse details |
Alexander DROBCHENKO, Joni-Kristian KAMARAINEN, Lasse LENSU, Jarkko VARTIAINEN, Heikki K?LVI?INEN, Tuomas EEROLA() |
Machine Vision and Pattern Recognition Laboratory, Department of Information Technology, Faculty of Technology Management, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland |
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Abstract Fine and sparse details appear in many quality inspection applications requiring machine vision. Especially on flat surfaces, such as paper or board, the details can be made detectable by oblique illumination. In this study, a general definition of such details is given by defining sufficient statistical properties from histograms. The statistical model allows simulation of data and comparison of methods designed for detail detection. Based on the definition, utilization of the existing thresholding methods is shown to be well motivated. The comparison shows that minimum error thresholding outperforms the other standard methods. Finally, the results are successfully applied to a paper printability inspection application, and the IGT picking assessment, in which small surface defects must be detected. The provided method and measurement system prototype provide automated assessment with results comparable to manual expert evaluations in this laborious task.
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Keywords
adaptive thresholding
paper quality inspection
picking
machine vision
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Corresponding Author(s):
EEROLA Tuomas,Email:tuomas.eerola@lut.fi
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Issue Date: 05 June 2011
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