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Computer Vision for X-Ray Testing pp 1–41Cite as

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/.

References

  1. Abidi, B.R., Zheng, Y., Gribok, A.V., Abidi, M.A.: Improving weapon detection in single energy X-ray images through pseudocoloring. IEEE Trans. Syst., Man, Cybern., Part C: Appl. Rev. 36(6), 784–796 (2006)

    CrossRef  Google Scholar 

  2. Agarwal, S., Snavely, N., Simon, I., Seitz, S., Szeliski, R.: Building Rome in a day. In: IEEE 12th International Conference on Computer Vision (ICCV2009), pp. 72–79 (2009)

    Google Scholar 

  3. Agoston, G.A.: The concept of color. Color Theory and Its Application in Art and Design, pp. 5–10. Springer, Berlin (1987)

    Google Scholar 

  4. Akcay, S., Breckon, T.: Towards automatic threat detection: a survey of advances of deep learning within X-ray security imaging (2020). arXiv:2001.01293

  5. Allain, M., Idier, J.: Efficient binary reconstruction for non destructive evaluation using gammagraphy. Inverse Prob. 4(23), 1371–1393 (2007)

    MathSciNet  CrossRef  Google Scholar 

  6. Als-Neielsen, J., McMorrow, D.: Elements of Modern X-Ray Physics, 2nd edn. Willey, Hoboken (2011)

    Google Scholar 

  7. Baştan, M., Byeon, W., Breuel, T.M.: Object recognition in multi-view dual x-ray images. In: British Machine Vision Conference BMVC (2013)

    Google Scholar 

  8. Baştan, M., Yousefi, M.R., Breuel, T.M.: Visual words on baggage X-ray images. Computer Analysis of Images and Patterns, pp. 360–368. Springer, Berlin (2011)

    Google Scholar 

  9. Bavendiek, K., Krause, A., Beyer, A.: Durchsatzerhöhung in der industriellen Röntgenprüfung – Eine Kombination aus innovativem Prüfablauf und optimierter Bildauswertung. In: DGZfP Jahrestagung, vol. Berichtsband 63.1, pp. 301–306. Deutsche Gesellschaft für Zerstörungsfreie Prüfung e.V., Bamberg (1998)

    Google Scholar 

  10. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    CrossRef  Google Scholar 

  11. Bhuyan, M.K.: Computer Vision and Image Processing: Fundamentals and Applications. CRC Press, Boca Raton (2019)

    Google Scholar 

  12. Bian, J., Siewerdsen, J., Han, X., Sidky, E., Prince, J., C., P., Pan, X.: Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT. Phys. Med. Biol. 55(22), 6575–6599 (2010)

    Google Scholar 

  13. Bishop, C.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)

    Google Scholar 

  14. Bouman, C., Sauer, K.: A unified approach to statistical tomography using coordinate descent optimization. IEEE Trans. Image Process., 480–492 (1996)

    Google Scholar 

  15. Bracewell, R.N.: Strip integration in radio astronomy. Aust. J. Phys. 9(2), 198–217 (1956)

    MathSciNet  CrossRef  Google Scholar 

  16. Bunke, J.: Computertomographie. In: Ewen, K. (ed.) Moderne Bildgebung: Physik, Gerätetechnik, Bildbearbeitung und -kommunikation, Strahlenschutz, Qualitätskontrolle, pp. 153–170. Georg Thieme Verlag, Stuttgart, New York (1998)

    Google Scholar 

  17. Buzug, T.: Computed Tomography. Springer, Berlin (2008)

    Google Scholar 

  18. Carmignato, S., Dewulf, W., Leach, R.: Industrial X-Ray Computed Tomography. Springer, Berlin (2018)

    Google Scholar 

  19. Carrasco, M., Pizarro, L., Mery, D.: Visual inspection of glass bottlenecks by multiple-view analysis. Int. J. Comput. Integr. Manuf. 23(10), 925–941 (2010)

    CrossRef  Google Scholar 

  20. Castleman, K.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (1996)

    Google Scholar 

  21. Cha, B.K., Jeon, S., Seo, C.W.: X-ray performance of a wafer-scale cmos flat panel imager for applications in medical imaging and nondestructive testing. Nucl. Instrum. Methods Phys. Res., Sect. A 831, 404–409 (2016)

    CrossRef  Google Scholar 

  22. Chouai, M., Merah, M., Sancho-GÓmez, J.L., Mimi, M.: A machine learning color-based segmentation for object detection within dual X-ray baggage images. In: Proceedings of the 3rd International Conference on Networking, Information Systems & Security, pp. 1–11 (2020)

    Google Scholar 

  23. Cullity, B.D., Stock, S.R.: Elements of X-Ray Diffraction. Pearson, London (2001)

    Google Scholar 

  24. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Conference on Computer Vision and Pattern Recognition (CVPR2005), vol. 1, pp. 886–893 (2005)

    Google Scholar 

  25. Dennhoven, M., Kunze, C., Kuehn, R.: Baggage inspection device (1977). US Patent 4,047,035

    Google Scholar 

  26. Donges, G., Dietrich, R.: Baggage inspection system (1988). US Patent 4,759,047

    Google Scholar 

  27. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    Google Scholar 

  28. Eshel, R., Moses, Y.: Tracking in a dense crowd using multiple cameras. Int. J. Comput. Vision 88, 129–43 (2010)

    CrossRef  Google Scholar 

  29. Faugeras, O., Luong, Q.T., Papadopoulo, T.: The Geometry of Multiple Images: The Laws that Govern the Formation of Multiple Images of a Scene and Some of their Applications. The MIT Press, Cambridge (2001)

    Google Scholar 

  30. Forsyth, D.A., Ponce, J.: A modern approach. A Modern Approach, Computer Vision (2003)

    Google Scholar 

  31. Franzel, T., Schmidt, U., Roth, S.: Object detection in multi-view X-Ray images. Pattern Recognit., 144–154 (2012)

    Google Scholar 

  32. Frikel, J.: Sparse regularization in limited angle tomography. Appl. Comput. Harmon. Anal. 1(34), 117–141 (2013)

    MathSciNet  CrossRef  Google Scholar 

  33. Gallavotti, G.: Statistical mechanics. Texts and Monographs in Physics. Springer, Berlin (1999)

    Google Scholar 

  34. Goebbels, J.: Computed tomography. Handbook of Technical Diagnostics, pp. 249–258. Springer, Berlin (2013)

    Google Scholar 

  35. Gonzalez, R., Woods, R.: Digital Image Processing, 3rd edn. Prentice Hall, Pearson (2008)

    Google Scholar 

  36. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    Google Scholar 

  37. Hadamard, J.: Lectures on the Cauchy Problem in Linear Partial Differential Equations. Yale University Press, New Haven (1923)

    Google Scholar 

  38. Hanke, R., Fuchs, T., Uhlmann, N.: X-ray based methods for non-destructive testing and material characterization. Nucl. Instrum. Methods Phys. Res., Sect. A 591(1), 14–18 (2008)

    CrossRef  Google Scholar 

  39. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  40. Hassabis, D., Kumaran, D., Summerfield, C., Botvinick, M.: Neuroscience-inspired artificial intelligence. Neuron 95(2), 245–258 (2017)

    CrossRef  Google Scholar 

  41. Heinzerling, J.: Bildverstärker-Fernseh-Kette. In: Ewen, K. (ed.) Moderne Bildgebung: Physik, Gerätetechnik, Bildbearbeitung und -kommunikation, Strahlenschutz, Qualitätskontrolle, pp. 115–126. Georg Thieme Verlag, Stuttgart, New York (1998)

    Google Scholar 

  42. Heinzerling, J.: Röntgenstrahler. In: Ewen, K. (ed.) Moderne Bildgebung: Physik, Gerätetechnik, Bildbearbeitung und -kommunikation, Strahlenschutz, Qualitätskontrolle, pp. 77–85. Georg Thieme Verlag, Stuttgart, New York (1998)

    Google Scholar 

  43. Heitz, G., Chechik, G.: Object separation in X-ray image sets. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2010), pp. 2093–2100 (2010)

    Google Scholar 

  44. Hellier, C.: Handbook of Nondestructive Evaluation, 2nd edn. McGraw Hill, New York (2013)

    Google Scholar 

  45. Herman, G., Lung, H.: Reconstruction from divergent beams: a comparison of algorithms with and without rebinning. Comput. Biol. Med. 10(2), 131–139 (1980)

    CrossRef  Google Scholar 

  46. Horbaschek, H.: Technologie und Einsatz von Festkörperdetektoren in der Röntgentechnik (1998). Vortrag der Firma Siemens Pforchheim in der 9. Sitzung des Unterausschusses Bildverarbeitung in der Durchstrhlungprüfung (UA BDS) der Deutschen Gesellschaft für Zerstörungsfreie Prüfung e.V. (DGZfP), Ahrensburg

    Google Scholar 

  47. Hubbell, J., Seltzer, S.: Tables of X-Ray mass attenuation coefficients and mass energy-absorption coefficients from 1 keV to 20 MeV for elements Z = 1 to 92 and 48 additional substances of dosimetric interest (1996). http://www.nist.gov/pml/data/xraycoef/index.cfm

  48. Iniewski, K.: CZT sensors for computed tomography: from crystal growth to image quality. J. Instrum. 11(12), C12,034 (2016)

    Google Scholar 

  49. Jaeger, T.: Optimierungsansätze zur Lösung des limited data problem in der Computertomographie. Verlag Dr. Köster, Berlin (1997)

    Google Scholar 

  50. Jaeger, T., Heike, U., Bavendiek, K.: Experiences with an amorphous silicon array detector in an ADR application. In: International Computerized Tomography for Industrial Applications and Image Processing in Radiology, DGZfP Proceedings BB 67-CD, pp. 111–114. Berlin (1999)

    Google Scholar 

  51. Klette, R.: Concise computer vision: an introduction into theory and algorithms. Springer Science & Business Media (2014)

    Google Scholar 

  52. Kolkoori, S., Wrobel, N., Deresch, A., Redmer, B., Ewert, U.: Dual high-energy X-ray digital radiography for material discrimination in cargo containers. In: 11th European Conference on Non-Destructive Testing (ECNDT 2014), 6–10 Oct 2014, Prague, Czech Republic (2014)

    Google Scholar 

  53. Konolige, K., Agrawal, M.: FrameSLAM: from bundle adjustment to realtime visual mapping. IEEE Trans. Rob. 24(5), 1066–1077 (2008)

    CrossRef  Google Scholar 

  54. Kosanetzky, J.M., Krüger, R.: Philips MU231: Räderprüfanlage. Technischer Bericht, Philips Industrial X-ray GmbH, Hamburg (1997)

    Google Scholar 

  55. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. NIPS, pp. 1106–1114 (2012)

    Google Scholar 

  56. Kuchling, H.: Taschenbuch der Physik, 12th edn. Harri Deutsch, Thun-Frankfurt, Main (1989)

    Google Scholar 

  57. Kuhl, D., Edwards, R.: Image separation radioisotope scanning. Radiology 80(4), 653–662 (1963)

    CrossRef  Google Scholar 

  58. Kunze, C., Dennhoven, M.: Inspection system for baggage (1980). US Patent 4,216,499

    Google Scholar 

  59. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    CrossRef  Google Scholar 

  60. LeCun, Y., Bottou, L., Bengio, Y.: Gradient-based learning applied to document recognition. In: Proceedings of the Third International Conference on Research in Air Transportation (1998)

    Google Scholar 

  61. Lehr, C., Liedtke, C.: 3D reconstruction of volume defects from few X-ray. Computer analysis of images and patterns, pp. 257–284. Springer, Berlin (1999)

    Google Scholar 

  62. Lossau, N.: Röntgen: Eine Entdeckung verändert unser Leben, 1 edn. Köln, vgs (1995)

    Google Scholar 

  63. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    CrossRef  Google Scholar 

  64. Martz, H.E., Logan, C.M., Schneberk, D.J., Shull, P.J.: X-Ray Imaging: Fundamentals, Industrial Techniques and Applications. CRC Press, Boca Raton (2016)

    Google Scholar 

  65. MathWorks: Image Processing Toolbox for Use with MATLAB: User’s Guide. The MathWorks Inc. (2014)

    Google Scholar 

  66. Matthes, E.: Python crash course: a hands-on, project-based introduction to programming. No Starch Press (2015)

    Google Scholar 

  67. Mery, D.: Explicit geometric model of a radioscopic imaging system. NDT & E Int. 36(8), 587–599 (2003)

    CrossRef  Google Scholar 

  68. Mery, D.: Exploiting multiple view geometry in X-ray testing: part I, theory. Mater. Eval. 61(11), 1226–1233 (2003)

    Google Scholar 

  69. Mery, D.: Automated detection in complex objects using a tracking algorithm in multiple X-ray views. In: Proceedings of the 8th IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum (OTCBVS 2011), in Conjunction with CVPR 2011, Colorado Springs, pp. 41–48 (2011)

    Google Scholar 

  70. Mery, D.: Aluminum casting inspection using deep learning: a method based on convolutional neural networks. J. Nondestr. Eval. 39(1), 12 (2020)

    CrossRef  Google Scholar 

  71. Mery, D., Filbert, D.: Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence. IEEE Trans. Robot. Autom. 18(6), 890–901 (2002)

    CrossRef  Google Scholar 

  72. Mery, D., Filbert, D., Jaeger, T.: Image processing for fault detection in aluminum castings. In: MacKenzie, D., Totten, G. (eds.) Analytical Characterization of Aluminum and Its Alloys. Marcel Dekker, New York (2003)

    Google Scholar 

  73. Mery, D., Jaeger, T., Filbert, D.: A review of methods for automated recognition of casting defects. Insight 44(7), 428–436 (2002)

    Google Scholar 

  74. Murphy, E.: A rising war on terrorists. IEEE Spectr. 26(11), 33–36 (1989)

    CrossRef  Google Scholar 

  75. Murray, N., Riordan, K.: Evaluation of automatic explosive detection systems. In: 29th Annual 1995 International Carnahan Conference on Security Technology, 1995. Proceedings. Institute of Electrical and Electronics Engineers, pp. 175 –179 (1995). https://doi.org/10.1109/CCST.1995.524908

  76. Neri, E., Caramella, D., Bartolozzi, C.: Image processing in radiology. In: Baert, A.L, Knauth, M., Sartor, K (eds.) Medical Radiology. Diagnostic Imaging. Springer, Berlin (2008)

    Google Scholar 

  77. Noble, A., Gupta, R., Mundy, J., Schmitz, A., Hartley, R.: High precision X-ray stereo for automated 3D CAD-based inspection. IEEE Trans. Robot. Autom. 14(2), 292–302 (1998)

    CrossRef  Google Scholar 

  78. Oldendorf, W.: Isolated flying spot detection of radiodensity discontinuities-displaying the internal structural pattern of a complex object. IRE Trans. Biomed. Electron. 8(1), 68–72 (1961)

    CrossRef  Google Scholar 

  79. Peters, T., Lewitt, R.: Computed tomography with fan beam geometry. J. Comput. Assist. Tomogr. 1(4), 429–436 (1977)

    Google Scholar 

  80. Peugeot, R.S.: X-ray baggage inspection system (1975). US Patent 3,919,467

    Google Scholar 

  81. Pichara, K., Pieringer, C.: Advanced Computer Programming in Python. CreateSpace Independent Publishing Platform (2017)

    Google Scholar 

  82. Purschke, M.: Radioskopie – Die Prüftechnik der Zukunft? In: DGZfP Jahrestagung, vol. Berichtsband 68.1, pp. 77–84. Deutsche Gesellschaft für Zerstörungsfreie Prüfung e.V., Celle (1999)

    Google Scholar 

  83. Purschke, M.: IQI-sensitivity and applications of flat panel detectors and X-ray image intensifiers - a comparison. Insight 44(10), 628–630 (2002)

    Google Scholar 

  84. Radon, J.: Über die Bestimmung von Funktionen durch ihre Integrale längs gewisser Mannigfaltigkeiten. Ber. Sächs. Akad. Wiss. Math. Phys. Kl. 69, 262–277 (1917)

    MATH  Google Scholar 

  85. Rantala, M., Vanska, S., Jarvenpaa, S., Kalke, M., Lassas, M., Moberg, J., Siltanen, S.: Wavelet-based reconstruction for limited-angle. IEEE Trans. Med. Imaging 25(2), 210–217 (2006)

    CrossRef  Google Scholar 

  86. Rebuffel, V., Dinten, J.M.: Dual-energy X-ray imaging: benefits and limits. Insight-Non-Destr. Test. Cond. Monit. 49(10), 589–594 (2007)

    CrossRef  Google Scholar 

  87. Retraint, F., Peyrin, F., Dinten, J.: Three-dimensional regularized binary image reconstruction from three two-dimensional projections using a randomized ICM algorithm. Int. J. Imaging Syst. Technol. 9, 135–146 (1998)

    CrossRef  Google Scholar 

  88. Richter, H.U.: Chronik der Zerstörungsfreien Materialprüfung, 1st edn. DGZfP, Verlag für Schweißen und verwendete Verfahren, DVS-Verlag GmbH, Berlin, Deutsche Gesellschaft für Zerstörungsfreie Prüfung (1999)

    Google Scholar 

  89. Riffo, V., Mery, D.: Active X-ray testing of complex objects. Insight 54(1), 28–35 (2012)

    CrossRef  Google Scholar 

  90. Röntgen, W.: Eine neue Art von Strahlen: I Mitteilung. In: Sitzungsbericht der Würzburger Physikal.-Medicin. Gesellschaft. Verlag und Druck der Stahel’schen K. Hof- und Universitäts- Buch- und Kunsthandlung, Würzburg (1895)

    Google Scholar 

  91. Rowlands, J.: The physics of computed radiography. Phys. Med. Biol. 47(23), R123 (2002)

    Google Scholar 

  92. Schaefer, M.: 100 Jahre Röntgenprüftechnik - Prüfsysteme früher und heute. In: DGZfP Jahrestagung, pp. 13–26. Deutsche Gesellschaft für Zerstörungsfreie Prüfung e.V., Aachen (1995)

    Google Scholar 

  93. Schwieger, R.: Stillegung, sicherer Einschluß und Abbau kerntechnischer Anlagen. Institut für Werkstoffkunde, Universität Hannover, Technischer Bericht (1999)

    Google Scholar 

  94. Shaw, Z.A.: Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code. Addison-Wesley Professional (2017)

    Google Scholar 

  95. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). ArXiv:abs/1409.1556

  96. Singh, M., Singh, S.: Optimizing image enhancement for screening luggage at airports. In: Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2005. CIHSPS 2005, pp. 131–136 (2005). https://doi.org/10.1109/CIHSPS.2005.1500627

  97. Singh, S., Singh, M.: Explosives detection systems (eds) for aviation security. Signal Process. 83(1), 31–55 (2003)

    Google Scholar 

  98. Soussen, C., Idier, J.: Reconstruction of three-dimensional localized objects from limited angle X-ray projections: an approach based on sparsity and multigrid image representation. J. Electron. Imaging 17(3) (2008)

    Google Scholar 

  99. Strecker, H.: Automatic detection of explosives in airline baggage using elastic X-ray scatter. Medicamundi 42, 30–33 (1998)

    Google Scholar 

  100. Su, H., Sun, M., Fei-Fei, L., Savarese, S.: Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories. In: International Conference on Computer Vision (ICCV2009) (2009)

    Google Scholar 

  101. Szeles, C., Soldner, S.A., Vydrin, S., Graves, J., Bale, D.S.: Cdznte semiconductor detectors for spectroscopic X-ray imaging. IEEE Trans. Nucl. Sci. 55(1), 572–582 (2008)

    CrossRef  Google Scholar 

  102. Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, New York Inc (2011)

    Google Scholar 

  103. Teubl, J., Bischof, H.: Comparison of Multiple View Strategies to Reduce False Positives in Breast Imaging. Digital Mammography, pp. 537–544 (2010)

    Google Scholar 

  104. Tian, Z., Jia, X., Yuan, K., Pan, T., Jiang, S.: Low-dose CT reconstruction via edge-preserving total variation regularization. Phys. Med. Biol. 56(18), 5949–5967 (2011)

    CrossRef  Google Scholar 

  105. Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vision 57(2), 137–154 (2004)

    CrossRef  Google Scholar 

  106. Völkel: Grundlagen für den Prüfer mit Röntgen- und Gammastrahlung (Durchstrahlungsprüfung). Amt für Standarisierung, Meßwesen und Warenprüfung, Fachgebiet Zerstörungsfreie Werkstoffprüfung (1989)

    Google Scholar 

  107. Wang, L.: Cross-section reconstruction with a fan-beam scanning geometry. IEEE Trans. Comput. 100(3), 264–268 (1977)

    Google Scholar 

  108. Wells, K., Bradley, D.: A review of X-ray explosives detection techniques for checked baggage. Applied Radiation and Isotopes (2012)

    Google Scholar 

  109. Yu, D., Fessler, J.: Edge-preserving tomographic reconstruction with nonlocal regularization. IEEE Trans. Med. Imaging 2(21), 159–173 (2002)

    CrossRef  Google Scholar 

  110. Zabler, S., Maisl, M., Hornberger, P., Hiller, J., Fella, C., Hanke, R.: X-ray imaging and computed tomography for engineering applications. tm-Technisches Messen 1(ahead-of-print) (2020)

    Google Scholar 

  111. Zhou, Y., Thibault, J., Bouman, C., Sauer, K., Hsieh, J.: Fast Model-Based X-ray CT Reconstruction Using Spatially Nonhomogeneous ICD Optimization. IEEE Trans. Image Process. 20(1), 161–175 (2011)

    MathSciNet  CrossRef  Google Scholar 

  112. Zografos, V., Nordberg, K., Ellis, L.: Sparse motion segmentation using multiple six-point consistencies. In: Proceedings of the Asian Conference on Computer Vision (ACCV2010) (2010)

    Google Scholar 

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  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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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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