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Proceedings Paper

Sensitivity analysis for texture models applied to rust steel classification
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Paper Abstract

The exposure of metallic structures to rust degradation during their operational life is a known problem and it affects storage tanks, steel bridges, ships, etc. In order to prevent this degradation and the potential related catastrophes, the surfaces have to be assessed and the appropriate surface treatment and coating need to be applied according to the corrosion time of the steel. We previously investigated the potential of image processing techniques to tackle this problem. Several mathematical algorithms methods were analyzed and evaluated on a database of 500 images. In this paper, we extend our previous research and provide a further analysis of the textural mathematical methods for automatic rust time steel detection. Statistical descriptors are provided to evaluate the sensitivity of the results as well as the advantages and limitations of the different methods. Finally, a selector of the classifiers algorithms is introduced and the ratio between sensitivity of the results and time response (execution time) is analyzed to compromise good classification results (high sensitivity) and acceptable time response for the automation of the system.

Paper Details

Date Published: 3 May 2004
PDF: 9 pages
Proc. SPIE 5303, Machine Vision Applications in Industrial Inspection XII, (3 May 2004); doi: 10.1117/12.526838
Show Author Affiliations
Maite Trujillo, Brunel Univ. (United Kingdom)
Mustapha Sadki, Brunel Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 5303:
Machine Vision Applications in Industrial Inspection XII
Jeffery R. Price; Fabrice Meriaudeau, Editor(s)

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