Abstract
In this chapter, we cover the main techniques of image processing used in X-ray testing. They are: (i) image processing to enhance details, (ii) image filtering to remove noise or detect high frequency details, (iii) edge detection to identify the boundaries of the objects, (iv) image segmentation to isolate the regions of interest and (v) to remove the blurriness of the X-ray image. The chapter provides an overview and presents several methodologies with examples using real and simulated X-ray images.
Cover image: Gradient of an X-ray image of a wheel (from X-ray image C0001_0001 colored with ‘jet’ colormap).
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Notes
- 1.
At high levels, the Poisson distribution approaches the Gaussian with a standard deviation equal to the square root of the mean: \(\sigma = \sqrt{\mu }\).
- 2.
A video of this small defect can be watched at http://youtu.be/e3wDJhq2Tqg.
- 3.
The video can be found in http://youtu.be/tWdJ-NFE6vY.
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Mery, D. (2015). X-ray Image Processing. In: Computer Vision for X-Ray Testing. Springer, Cham. https://doi.org/10.1007/978-3-319-20747-6_4
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DOI: https://doi.org/10.1007/978-3-319-20747-6_4
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