Computer vision for X-Ray testing: Imaging, systems, image databases, and algorithms

Mery D.; Pieringer C.

Keywords: reliability, ray testing, Algorithms; Computer Vision; Deep Learning; Dual Energy; Image Analysis; Image Processing; Non, Destructive Testing; Pattern Recognition; Python; Quality control, safety and risk; Simulation; X

Abstract

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.

Más información

Título según SCOPUS: Computer vision for X-Ray testing: Imaging, systems, image databases, and algorithms
Título de la Revista: Computer Vision for X-Ray Testing: Imaging, Systems, Image Databases, and Algorithms
Editorial: Springer International Publishing
Fecha de publicación: 2020
Página final: 456
Idioma: English
DOI:

10.1007/978-3-030-56769-9

Notas: SCOPUS