Publicaciones

Christian Pieringer

RL1, Publisher:, Link>

AUTHORS

Christian Pieringer, Domingo Mery

ABSTRACT

In this chapter, we will cover known classifiers that can be used in X-ray testing. Several examples will be presented using Python. 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.

 

14 visualizaciones Ir a la publicación

RL1, Publisher:, Link>

AUTHORS

Christian Pieringer, Domingo Mery

ABSTRACT

In this chapter, we present the dataset that is used in this book to illustrate and test several methods. The database consists of 23,189 X-ray images. The images are organized in a public database called GDXray+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 GDXray+is 4.5 GB and it can be downloaded from our website.


14 visualizaciones Ir a la publicación

RL1, Publisher:, Link>

AUTHORS

Christian Pieringer, Domingo Mery

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) and simulation of threat objects (that can be used to assess the performance of computer vision methods, to enhance the training dataset, or to improve a training program for human inspectors). Afterwards, the chapter gives an overview of the use of Generative Adversarial Networks (GANs) in the simulation of realistic X-ray images. Finally, we present ‘aRTist’, a simulation software that can be used to generate very realistic X-ray images. The chapter also has some Python examples that the reader can run and follow easily.


11 visualizaciones Ir a la publicación

RL1, Publisher:, Link>

AUTHORS

Christian Pieringer, Domingo Mery

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. It is worthwhile to mention, that the features mentioned in this chapter are called handcrafted features, in contrast to the learned features that are explained in Chap. 7 using deep learning techniques. Finally, the chapter addresses some feature selection techniques that can be used to chose which features are relevant in terms of extraction.


16 visualizaciones Ir a la publicación

RL1, Publisher:, Link>

AUTHORS

Christian Pieringer, Domingo Mery

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 the 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 semi-automated 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 the last decades. In this chapter, we offer an introduction to our book by covering relevant issues of X-ray testing.


37 visualizaciones Ir a la publicación

RL1, Publisher:, Link>

AUTHORS

Christian Pieringer, Domingo Mery

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 Python are given in order to illustrate the performance of the methods.


19 visualizaciones Ir a la publicación