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2015 | Buch

X-ray Testing Kapitel lesen Erstes Kapitel lesen

Computer Vision for X-Ray Testing

Imaging, Systems, Image Databases, and Algorithms

verfasst von: Domingo Mery

Verlag: Springer International Publishing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. X-ray Testing
Abstract
X-ray testing has been developed for the inspection of materials or objects, where the aim is to analyze—nondestructively—those inner parts that are undetectable to the naked eye. Thus, X-ray testing is used to determine if a test object deviates from a given set of specifications. Typical applications are inspection of automotive parts, quality control of welds, baggage screening, analysis of food products, inspection of cargos, and quality control of electronic circuits. In order to achieve efficient and effective X-ray testing, automated and semiautomated systems based on computer vision algorithms are being developed to execute this task. In this book, we present a general overview of computer vision approaches that have been used in X-ray testing. In this chapter, we offer an introduction to our book by covering relevant issues of X-ray testing.
Domingo Mery
Chapter 2. Images for X-ray Testing
Abstract
In this chapter, we present the dataset that is used in this book to illustrate and test several methods. The database consists of 19,407 X-ray images. The images are organized in a public database called \(\mathbb {GDX}\)ray that can be used free of charge, but for research and educational purposes only. The database includes five groups of X-ray images: castings, welds, baggage, natural objects, and settings. Each group has several series, and each series several X-ray images. Most of the series are annotated or labeled. In such cases, the coordinates of the bounding boxes of the objects of interest or the labels of the images are available in standard text files. The size of \(\mathbb {GDX}\)ray is 3.5 GB and it can be downloaded from our website.
Domingo Mery
Chapter 3. Geometry in X-ray Testing
Abstract
Geometry is of basic importance for understanding in X-ray testing. In this chapter, we present a mathematical background of the monocular and multiple view geometry which is normally used in X-ray computer vision systems. The chapter describes an explicit model which relates the 3D coordinates of an object to the 2D coordinates of the digital X-ray image pixel, the calibration problem, the geometric and algebraic constraints between two, three, and more X-ray images taken at different projections of the object, and the problem of 3D reconstruction from n views.
Domingo Mery
Chapter 4. X-ray Image Processing
Abstract
In this chapter, we cover the main techniques of image processing used in X-ray testing. They are: (i) image processing to enhance details, (ii) image filtering to remove noise or detect high frequency details, (iii) edge detection to identify the boundaries of the objects, (iv) image segmentation to isolate the regions of interest and (v) to remove the blurriness of the X-ray image. The chapter provides an overview and presents several methodologies with examples using real and simulated X-ray images.
Domingo Mery
Chapter 5. X-ray Image Representation
Abstract
In this chapter we cover several topics that are used to represent an X-ray image (or a specific region of an X-ray image). This representation means that new features are extracted from the original image that can give us more information than the raw information expressed as a matrix of gray values. This kind of information is extracted as features or descriptors, i.e., a set of values, that can be used in pattern recognition problems such as object recognition, defect detection, etc. The chapter explains geometric and intensity features, and local descriptors and sparse representations that are very common in computer vision applications. Furthermore, the chapter addresses some feature selection techniques that can be used to chose which features are relevant in terms of extraction.
Domingo Mery
Chapter 6. Classification in X-ray Testing
Abstract
In this chapter we will cover known classifiers that can be used in X-ray testing. Several examples will be presented using Matlab. The reader can easily modify the proposed implementations in order to test different classification strategies. We will then present how to estimate the accuracy of a classifier using hold-out, cross-validation, and leave-one-out. Finally, we will present an example that involves all steps of a pattern recognition problem, i.e., feature extraction, feature selection, classifier’s design, and evaluation. We will thus propose a general framework to design a computer vision system in order to select—automatically—from a large set of features and a bank of classifiers, those features and classifiers that can achieve the highest performance.
Domingo Mery
Chapter 7. Simulation in X-ray Testing
Abstract
In order to evaluate the performance of computer vision techniques, computer simulation can be a useful tool. In this chapter we review some basic concepts of the simulation of X-ray images, and present simple geometric and imaging models that can be used in the simulation. We explain the basic simulation principles and we address some techniques of simulated defects (that can be used to assess the performance of a computer vision method for automated defect recognition). The chapter also has some Matlab examples that the reader can run and follow, along with examples of simulated defects in castings and welds.
Domingo Mery
Chapter 8. Applications in X-ray Testing
Abstract
In this chapter, relevant applications on X-ray testing are described. We cover X-ray testing in (i) castings, (ii) welds, (iii) baggage, (iv) natural products, and (v) others (like cargos and electronic circuits). For each application, the state of the art is presented. Approaches in each application are summarized showing how they use computer vision techniques. A detailed approach is shown in each application and some examples using Matlab are given in order to illustrate the performance of the methods.
Domingo Mery
Backmatter
Metadaten
Titel
Computer Vision for X-Ray Testing
verfasst von
Domingo Mery
Copyright-Jahr
2015
Electronic ISBN
978-3-319-20747-6
Print ISBN
978-3-319-20746-9
DOI
https://doi.org/10.1007/978-3-319-20747-6

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