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