@article{1433915,
      author = {Mery, Domingo, and Pieringer, Christian,},
      url = {http://library.usi.edu/record/1433915},
      title = {Computer vision for X-ray testing : imaging, systems,  image databases, and algorithms /},
      abstract = {Building on its strengths as a uniquely accessible  textbook combining computer vision and X-ray testing, this  enhanced second edition now firmly addresses core  developments in deep learning and vision, providing  numerous examples and functions using the Python language.  Covering complex topics in an easy-to-understand way,  without requiring any prior knowledge in the field, the  book provides a concise review of the key methodologies in  computer vision for solving important problems in  industrial radiology. The theoretical coverage is  strengthened with easily written code examples that the  reader can modify when developing new functions for X-ray  testing. Topics and features: Describes the core techniques  for image processing used in X-ray testing, including image  filtering, edge detection, image segmentation and image  restoration Incorporates advances in deep learning,  including aspects regarding convolutional neural networks,  transfer learning, and generative adversarial networks  Provides more than 65 examples in Python, and is supported  by an associated website, including a database of X-ray  images and a freely available Matlab toolbox Includes new  advances in simulation approaches for baggage inspection,  simulated X-ray imaging, and simulated structures (such as  defects and threat objects) Presents a range of different  representations for X-ray images, explaining how these  enable new features to be extracted from the original image  Examines a range of known X-ray image classifiers and  classification strategies, and techniques for estimating  the accuracy of a classifier Reviews a variety of  applications for X-ray testing, from industrial inspection  and baggage screening to the quality control of natural  products This classroom-tested and hands-on text/guidebook  is ideal for advanced undergraduates, graduates, and  professionals interested in practically applying image  processing, pattern recognition and computer vision  techniques for non-destructive quality testing and security  inspection. Dr. Domingo Mery is a Full Professor at the  Machine Intelligence Group (GRIMA) of the Department of  Computer Sciences, and Director of Research and Innovation  at the School of Engineering, at the Pontifical Catholic  University of Chile, Santiago, Chile. Dr. Christian  Pieringer is an Adjunct Instructor at the same  institution.},
      doi = {https://doi.org/10.1007/978-3-030-56769-9},
      recid = {1433915},
      pages = {1 online resource (xxvi, 456 pages) :},
}