Frontiers of Electrical and Electronic Engineering >
Thresholding-based detection of fine and sparse details
Received date: 24 Jun 2010
Accepted date: 26 Jan 2011
Published date: 05 Jun 2011
Copyright
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.
Key words： adaptive thresholding; paper quality inspection; picking; machine vision
Alexander DROBCHENKO , Joni-Kristian KAMARAINEN , Lasse LENSU , Jarkko VARTIAINEN , Heikki KÄLVIÄINEN , Tuomas EEROLA . Thresholding-based detection of fine and sparse details[J]. Frontiers of Electrical and Electronic Engineering, 2011 , 6(2) : 328 -338 . DOI: 10.1007/s11460-011-0139-x
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