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Proceedings Paper

Delineation of defects in noisy ternary images using a piecewise dynamic approach
Author(s): Mark Bradshaw
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Paper Abstract

Machine vision systems are increasingly being applied to the apparel industry for the inspection of web fabrics', complete garments2, lace3 and others. This paper describes a system component (delineation) from an automatic web fabric inspection system developed at De Montfort University, which is described more fully elsewhere1. A typical approach to detecting defects is to apply a threshold to the electronic signal generated by a CCD camera scanning the surface. The surface structure of the material introduces a noise component which tends to mask the message signals arising due to defects, making them hard to detect. The presence of a defect causes the signal to temporarily deviate from its mean noise position, and a dual threshold can be used to detect these deviations and generate message triggers, as shown in figure 1. The position of the thresholds can be determined by trading off the probability of false alarm with the probability of a correction detection. To detect defects of low contrast, the thresholds must be placed close to the noise mean, but this dramatically increases the probability of a false alarm trigger arsing. The ternary signal then undergoes a stage of filtering to remove isolated (and hence probably noise) triggers.

Paper Details

Date Published: 27 March 1995
PDF: 9 pages
Proc. SPIE 2423, Machine Vision Applications in Industrial Inspection III, (27 March 1995); doi: 10.1117/12.205520
Show Author Affiliations
Mark Bradshaw, De Montfort Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 2423:
Machine Vision Applications in Industrial Inspection III
Frederick Y. Wu; Stephen S. Wilson, Editor(s)

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