A1 Journal article (refereed), original research

Thresholding based Detection of Fine and Sparse Details

Publication Details
Authors: Drobchenko Alexander, Kämäräinen Joni-Kristian, Lensu Lasse, Vartiainen Jarkko, Kälviäinen Heikki, Eerola Tuomas
Publication year: 2011
Language: English
Related Journal or Series Information: Frontiers of Electrical and Electronic Engineering in China
Volume number: 6
Issue number: 2
ISSN: 1673-3460
eISSN: 1673-3584
JUFO-Level of this publication:
Open Access: Not an Open Access publication

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.

Last updated on 2017-22-03 at 14:52