Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.501
Detection of defects in metallic pieces is an important application in the field of Non-Destructive Testing (NDT), particularly in an industrial setting. These defects are mainly due to manufacturing errors or welding processes. In this article we will focus on this second category of defects using segmentation techniques applied to thewelded joints. Segmentation is one of the most difficulttasks in image processing, particularly in the case of noisy or low contrast images such as radiographic images of welds. In segmenting this type of image, many researchers have used neural networks and fuzzy logic methods. The results are impressive, however the methods require a complex implementation and are time consuming. In this work, we propose a new method for segmenting digitized radiographic images which is based on histogram analysis, contrast enhancement and image thresholding.Computing time is optimized by using integral images to calculate the local thresholds. Although the method gives comparable results to those obtained by previous methods in terms of visual segmentation quality, it is significantly simpler to implement.
image segmentation, thresholding, radiography, weld defects
Abdelhak Mahmoudi, "Welding Defect Detection by Segmentation of Radiographic Images", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 111-115, doi:10.1109/CSIE.2009.501