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X-ray Testing

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Abstract

X-ray testing has been developed for the inspection of materials or objects, where the aim is to analyze—nondestructively—those inner parts that are undetectable to the naked eye. Thus, X-ray testing is used to determine if a test object deviates from a given set of specifications. Typical applications are the inspection of automotive parts, quality control of welds, baggage screening, analysis of food products, inspection of cargos, and quality control of electronic circuits. In order to achieve efficient and effective X-ray testing, automated and semi-automated systems based on computer vision algorithms are being developed to execute this task. In this book, we present a general overview of computer vision approaches that have been used in X-ray testing in the last decades. In this chapter, we offer an introduction to our book by covering relevant issues of X-ray testing.

Cover image: X-ray images of woods (series N0010 colored with ‘hot’ colormap).

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Notes

  1. 1.

    Computed tomography is beyond the scope of this book due to space considerations, however, some simple examples and basic concepts are covered (see Sect. 1.6.5). For NDT applications using CT, the reader is referred to [18, 34].

  2. 2.

    As explained in Sect. 1.3, X-rays can be absorbed or scattered by the test object. In this book we present only the first interaction because scattering is not commonly used for X-ray testing applications covered in this book. For an interesting application based on the X-ray scattering effect, the reader is referred to [108].

  3. 3.

    pyxvis Library is an open source Python library that is used in all examples of this book (see Sect. 1.7.1).

  4. 4.

    An example of a video generated with dynamic color is shown on https://youtu.be/Vsxff5CuTO0.

  5. 5.

    Many reconstruction approaches assume parallel-beam geometry, whereas CT scanners usually employ fan-beam geometries. There are dedicated fan-beam algorithms (see, for example, [45]), however, there are methods that resample the fan-beam data in order to obtain an equivalent parallel-beam data (see, for example, [45, 79, 107]). Thus, traditional reconstruction approaches can be used.

  6. 6.

    See https://domingomery.ing.puc.cl/material/.

  7. 7.

    There are many textbooks that can be used to learn Python, see, for example, [66, 81, 94].

  8. 8.

    pyxvis Library is available on https://github.com/computervision-xray-testing/pyxvis, with all experiments implemented in Google Colab.

  9. 9.

    \(\mathbb {GDX}\)ray+is available on https://domingomery.ing.puc.cl/material/gdxray/.

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Mery, D., Pieringer, C. (2021). X-ray Testing. In: Computer Vision for X-Ray Testing. Springer, Cham. https://doi.org/10.1007/978-3-030-56769-9_1

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