Computer Vision for X-ray Testing
Second Edition (Jan, 2021):
by Domingo Mery and Christian Pieringer
- Presents a focus on the most important real-world applications of X-ray testing
- Updated edition featuring new material on deep learning, simulation approaches, and dual energy X-ray images
- Includes numerous examples in Python
- Provides supplementary material at an associated website, including slides, videos, and a database of X-ray images
- This accessible textbook presents an introduction to computer vision algorithms for industrially-relevant applications of X-ray testing.
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 supported by numerous examples, each of which can be tested and evaluated by the reader using a freely-available Matlab toolbox and X-ray image database.
[ DOWNLOAD ]
Topics and features
- Introduces the mathematical background for monocular and multiple view geometry, which is commonly used in X-ray computer vision systems
- Describes the main techniques for image processing used in X-ray testing, including image filtering, edge detection, image segmentation and image restoration
- 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 (including deep learning methodologies)
- 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 Python Library for use with the book’s many examples
This classroom-tested and hands-on guide is ideal for graduate and advanced undergraduate students interested in the practical application of image processing, pattern recognition and computer vision techniques for non-destructive quality testing and security inspection.
Code and Dataset
- Code: PyXvis (Library in Python for the second edition)
- Code: Xvis (Toolbox in Matlab for the first edition)
- Dataset: GDXray