Titlebook: Computer Vision for X-Ray Testing; Imaging, Systems, Im Domingo Mery,Christian Pieringer Textbook 2021Latest edition Springer Nature Switze

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X-ray Testing,on 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.

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Images for X-ray Testing,ral X-ray images. Most of the series are annotated or labeled. In such cases, the coordinates of the bounding boxes of the objects of interest or the labels of the images are available in standard text files. The size of .ray+is 4.5 GB and it can be downloaded from our website.

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Classification in X-Ray Testing,oblem, i.e., feature extraction, feature selection, classifier’s design, and evaluation. We will thus propose a general framework to design a computer vision system in order to select—automatically—from a large set of features and a bank of classifiers, those features and classifiers that can achieve the highest performance.

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https://doi.org/10.1007/978-1-4899-4854-0Networks (GANs) in the simulation of realistic X-ray images. Finally, we present ‘aRTist’, a simulation software that can be used to generate very realistic X-ray images. The chapter also has some Python examples that the reader can run and follow easily.

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