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Proceedings Paper

Image quality in automated visual web inspection
Author(s): Jyrki Laitinen
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Paper Abstract

In this study the relation between the performance of an imaging unit of a web inspection system and the final image quality is discussed. The basic idea is to analyze the results of the segmentation and feature extraction of defects in sample images as a function of imaging parameters. Determination of the quality of imaging and examples of the performance of a typical imaging unit are reviewed. The effect of the image quality on segmentation of the defects and feature extraction is analyzed in two cases: (1) The detection of small and low-contrast defects in paper inspection and (2) the depth of field considerations in steel inspection. Samples picked from the industrial manufacturing process are imaged using different imaging parameters and the defect areas in the images are segmented in order to illustrate the dependence of the system performance on the quality of imaging. Several segmentation methods are applied. These include direct thresholding, edge-based filtering, matched filtering and morphological filtering. The contrast of certain type of defects can be improved before segmentation by averaging the input data line by line. The signal processing methods presented here are computationally simple due to the need for high-speed real-time implementation in practical inspection.

Paper Details

Date Published: 15 April 1997
PDF: 12 pages
Proc. SPIE 3029, Machine Vision Applications in Industrial Inspection V, (15 April 1997); doi: 10.1117/12.271250
Show Author Affiliations
Jyrki Laitinen, VTT Automation (Finland)

Published in SPIE Proceedings Vol. 3029:
Machine Vision Applications in Industrial Inspection V
A. Ravishankar Rao; Ning S. Chang, Editor(s)

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