Computer Vision for X-Ray Testing: Imaging, Systems, Image Databases, and Algorithms - Hardcover

Mery, Domingo; Pieringer, Christian

Hardcover

ISBN 10: 3030567680 ISBN 13: 9783030567682

Publisher: Springer, 2020

Synopsis

[FIRST EDITION] This accessible textbook presents an introduction to computer vision algorithms for industrially-relevant applications of X-ray testing. Features: introduces the mathematical background for monocular and multiple view geometry; describes the main techniques for image processing used in X-ray testing; 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; discusses some basic concepts for the simulation of X-ray images and presents simple geometric and imaging models that can be used in the simulation; reviews a variety of applications for X-ray testing, from industrial inspection and baggage screening to the quality control of natural products; provides supporting material at an associated website, including a database of X-ray images and a Matlab toolbox for use with the book’s many examples.

About this title

About the Author

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.

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2020
• Language: English
• ISBN 10: 3030567680
• ISBN 13: 9783030567682
• Binding: Hardcover
• Edition number: 2
• Number of pages: 482