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Computer Vision for X-Ray Testing pp 169–226Cite as

X-ray Image Representation

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Abstract

In this chapter, we cover several topics that are used to represent an X-ray image (or a specific region of an X-ray image). This representation means that new features are extracted from the original image that can give us more information than the raw information expressed as a matrix of gray values. This kind of information is extracted as features or descriptors, i.e., a set of values, that can be used in pattern recognition problems such as object recognition, defect detection, etc. The chapter explains geometric and intensity features, and local descriptors and sparse representations that are very common in computer vision applications. It is worthwhile to mention, that the features mentioned in this chapter are called handcrafted features, in contrast to the learned features that are explained in Chap. 7 using deep learning techniques. Finally, the chapter addresses some feature selection techniques that can be used to chose which features are relevant in terms of extraction.

Cover image: Welding defects (from X-ray image W0001_0001, well known as BAM5, colored with ‘sinmap’ colormap).

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Notes

  1. 1.

    BSIF has been originally implemented in Matlab [27]. A Python implementation of BSIF is given at https://github.com/CVRL/OpenSourceIrisPAD that was used for iris recognition [11].

  2. 2.

    Sometimes the magnitude of the angle is used.

  3. 3.

    A good library for sparse representation in computer vision is SPAMS (SPArse Modelling Software) [34], see Matlab, Python and R implementations on http://spams-devel.gforge.inria.fr.

  4. 4.

    OMP is a greedy algorithm that iteratively selects locally optimal basis vectors [59].

  5. 5.

    In pybalu library, (5.40) is implemented in of pybalu library.

  6. 6.

    Classifiers and accuracy estimation are covered in Chap. 6.

  7. 7.

    The available names of models are (logistic regression), (Ridge function), (linear discriminant analysis), (quadratic discriminant analysis), (SVM classifier with linear kernel), (SVM classifier with RBF kernel).

  8. 8.

    Cross-validation is explained in Sect. 6.3.2.

  9. 9.

    In this example, we used an augmented version of this subset that is available in the folder . The original subset has 80 samples per class for training and 20 samples per class per testing. In the training stage of our example, the 80 samples per class are augmented to 320 per class by rotating them in \(0^0, 90^0, 180^0\), and \(270^0\). The testing samples of our examples correspond to the 20 samples per class of the original dataset (with no augmentation).

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Authors and Affiliations

  1. Department of Computer Science, Pontifical Catholic University of Chile, Macul, Santiago, Chile

    Domingo Mery

  2. Independent Research Scientist and Consultant—Artificial Intelligence, Santiago, Chile

    Christian Pieringer

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  1. Domingo Mery
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Mery, D., Pieringer, C. (2021). X-ray Image Representation. In: Computer Vision for X-Ray Testing. Springer, Cham. https://doi.org/10.1007/978-3-030-56769-9_5

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