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Computer Vision for X-Ray Testing pp 267–325Cite as

Applications in X-ray Testing

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Abstract

In this chapter, relevant applications on X-ray testing are described. We cover X-ray testing in (i) castings, (ii) welds, (iii) baggage, (iv) natural products, and (v) others (like cargos and electronic circuits). For each application, the state of the art is presented. Approaches in each application are summarized showing how they use computer vision techniques. A detailed approach is shown in each application and some examples using Matlab are given in order to illustrate the performance of the methods.

Cover image: 3D representation of the X-ray image of a wheel (X-ray image C0023_0001 colored with ‘sinmap’ colormap).

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Notes

  1. 1.

    The gradient image is computed by taking the square root of the sum of the squares of the gradient in a horizontal and vertical direction. These are calculated by the convolution of the X-ray image with the first derivative (in the corresponding direction) of the Gaussian low-pass filter used in the LoG-filter.

  2. 2.

    It is possible to use a CAD model of the casting to evaluate this criterion more precisely. Using this model, we could discriminate a small hole of the regular structure that is identified as a potential flaw. Additionally, the CAD model can be used to inspect the casting geometry, as shown in [15].

  3. 3.

    The saliency function is implemented in Xsaliency (see Appendix B) of \(\mathbb {X}\) VIS Toolbox.

  4. 4.

    We use in our experiments a fast implementation of multiple view geometry algorithms from Balu Toolbox [83].

  5. 5.

    We used in our experiments fast implementations of SIFT and KNN (based on k-d tree) from VLFeat Toolbox [85].

  6. 6.

    We used in our experiments a fast implementation of Mean Shift from PMT Toolbox [86].

  7. 7.

    The images tested in our experiments come from public GDXray database [87].

  8. 8.

    In this problem, the projective factorization can be used as well [14], however, our simplifying assumption is that only small depth variations occur and an affine model may be used.

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    Domingo Mery

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Mery, D. (2015). Applications in X-ray Testing. In: Computer Vision for X-Ray Testing. Springer, Cham. https://doi.org/10.1007/978-3-319-20747-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20746-9

  • Online ISBN: 978-3-319-20747-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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