By Domingo Mery
This available textbook provides an advent to computing device imaginative and prescient algorithms for industrially-relevant purposes of X-ray trying out. positive aspects: introduces the mathematical historical past for monocular and a number of view geometry; describes the most options for picture processing utilized in X-ray checking out; offers quite a number varied representations for X-ray photos, explaining how those let new beneficial properties to be extracted from the unique photo; examines a number of identified X-ray snapshot classifiers and class options; discusses a few simple strategies for the simulation of X-ray photos and offers uncomplicated geometric and imaging types that may be utilized in the simulation; reports numerous purposes for X-ray checking out, from business inspection and luggage screening to the standard keep watch over of usual items; offers helping fabric at an linked site, together with a database of X-ray photos and a Matlab toolbox to be used with the book’s many examples.
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Extra info for Computer Vision for X-Ray Testing: Imaging, Systems, Image Databases, and Algorithms
Moderne Bildgebung: Physik, Gerätetechnik, Bildbearbeitung und -kommunikation, Strahlenschutz, Qualitätskontrolle, pp. 77–85. Georg Thieme Verlag, Stuttgart (1998) 22. : Stillegung, sicherer Einschluß und Abbau kerntechnischer Anlagen. Institut für Werkstoffkunde, Universität Hannover, Technischer Bericht (1999) 23. : Philips MU231: Räderprüfanlage. Technischer Bericht, Philips Industrial X-ray GmbH, Hamburg (1997) 24. : Technologie und Einsatz von Festkörperdetektoren in der Röntgentechnik (1998).
Thus, we can use the power of human vision that can distinguish thousands of colors . In order to improve the visualization of an X-ray image, pseudo coloring can be used. In pseudo coloring, a gray value is converted into a color value. That is, we need a map function that relates the gray value x with a color value (R(x), G(x), B(x)) for red, green and blue respectively if we use a RGB-based color map . Some examples of the color maps are illustrated in Fig. 18 in which the transformations (R(x), G(x), B(x)) are shown for ‘jet’, ‘hsv’, ‘parula’, ‘hot’, ‘rainbow’, and ‘sinmap’ [27, 29, 30].
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, pp. 41–48. Colorado Springs (2011) 48. : Robust real-time object detection. Int. J. Comput. Vis. 57(2), 137–154 (2004) 49. : Histograms of oriented gradients for human detection. Conf. Comput. Vis. Pattern Recognit. (CVPR2005) 1, 886–893 (2005) 50. : Active X-ray testing of complex objects.
Computer Vision for X-Ray Testing: Imaging, Systems, Image Databases, and Algorithms by Domingo Mery