Performance assessment of multi-level image thresholding for paper quality inspection

Valentina Caldarelli, Luca Ceccarelli, Francesco Bianconi, Stefano A. Saetta and Antonio Fernández

International Journal of Service and Computing Oriented Manufacturing, 2014, vol. 1, issue 4, 281-294

Abstract: Automatic characterisation and detection of dirt particles in pulp and paper plays a pivotal role in the papermaking industry. Machine vision provides many potential advantages in terms of speed, accuracy and repeatability. Such systems make use of image processing algorithms which aim at separating paper and pulp impurities from the background. The most common approach is based on image thresholding, which consists of determining a set of intensity values that split an image into one or more classes, each representing either the background (i.e., an area with no defects) or an area with some types of contraries. In this paper, we present a quantitative experimental evaluation of four image thresholding methods (i.e., Otsu's, Kapur's, Kittler's and Yen's) for dirt analysis in paper. The results show that Kittler's method is the most stable and reliable for this task.

Keywords: machine vision; image thresholding; performance evaluation; paper quality inspection; defect detection; dirt particles; pulp and paper industry; image processing; paper impurities; dirt analysis. (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=66488 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijscom:v:1:y:2014:i:4:p:281-294

Access Statistics for this article

More articles in International Journal of Service and Computing Oriented Manufacturing from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().