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Proceedings Paper

Fast detection of line features in large images
Author(s): Thomas B. Sebastian; Kai F. Goebel; Tahs Saleh
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Paper Abstract

This paper addresses the automated detection of line features in large industrial inspection images. The manual examination of these images is labor-intensive and causes undesired delay of inspection results. Hence, it is desirable to automatically detect certain features of interest. In this paper we are concerned with the detection of vertical or slanted line features that appear at unpredictable intervals across the image. The line features may appear distorted due to shortcomings of the sensor and operator conditions. Line features are modeled as a pair of smoothed step edges of opposite polarity that are in close proximity, and two operators are used to detect them. The individual operator-outputs are combined in a non-linear fashion to form the line-feature response. The line features are then obtained by following the ridge of the line-feature response. In experiments on four datasets, over 98.8% of line features are correctly detected, with a low false-positive rate. Experiments also show that the approach works well in the presence of considerable noise due to poor operating conditions or sensor failure.

Paper Details

Date Published: 24 February 2005
PDF: 8 pages
Proc. SPIE 5679, Machine Vision Applications in Industrial Inspection XIII, (24 February 2005); doi: 10.1117/12.591649
Show Author Affiliations
Thomas B. Sebastian, GE Global Research Ctr. (United States)
Kai F. Goebel, GE Global Research Ctr. (United States)
Tahs Saleh, General Electric Co. (United Kingdom)


Published in SPIE Proceedings Vol. 5679:
Machine Vision Applications in Industrial Inspection XIII
Jeffery R. Price; Fabrice Meriaudeau, Editor(s)

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