An Infrared Thermal Image Processing Framework for Defect Detection of a Metal Part with Rough Surface

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We propose an infrared thermal image processing framework based on a modified fuzzy c-means clustering algorithm with revised similarity measure in this paper. The framework can realize the defect detection of a metal part with rough surface. Firstly, a comprehensive method is used to preprocess infrared thermal image. Secondly, the preprocessed image is segmented using modified fuzzy c-means clustering algorithm with revised similarity measure. Finally, taking the average gray level of each cluster in the original gray scale image as a feature, defect cluster is recognized. Experimental result shows that the proposed framework has very promising performance and can obtain precise information of defects on a metal part with rough surface.

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Periodical:

Edited by:

Mohamed Othman

Pages:

1356-1360

Citation:

J. Xie et al., "An Infrared Thermal Image Processing Framework for Defect Detection of a Metal Part with Rough Surface", Applied Mechanics and Materials, Vols. 229-231, pp. 1356-1360, 2012

Online since:

November 2012

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$38.00

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