X-ray Testing

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.

[1]  D. Bale,et al.  CdZnTe Semiconductor Detectors for Spectroscopic X-ray Imaging , 2008, IEEE Transactions on Nuclear Science.

[2]  Theobald Fuchs,et al.  X-ray based methods for non-destructive testing and material characterization , 2008 .

[3]  Yael Moses,et al.  Tracking in a Dense Crowd Using Multiple Cameras , 2010, International Journal of Computer Vision.

[4]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[5]  Vasileios Zografos,et al.  Sparse Motion Segmentation Using Multiple Six-Point Consistencies , 2010, ACCV Workshops.

[6]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[7]  Domingo Mery Explicit geometric model of a radioscopic imaging system , 2003 .

[8]  B.R. Abidi,et al.  Improving Weapon Detection in Single Energy X-Ray Images Through Pseudocoloring , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Domingo Mery,et al.  Automated detection in complex objects using a tracking algorithm in multiple X-ray views , 2011, CVPR 2011 WORKSHOPS.

[10]  Richard Szeliski,et al.  Building Rome in a day , 2009, ICCV.

[11]  Stefan Roth,et al.  Object Detection in Multi-view X-Ray Images , 2012, DAGM/OAGM Symposium.

[12]  Gal Chechik,et al.  Object separation in x-ray image sets , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[14]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Reinhard Klette,et al.  Concise Computer Vision , 2014, Undergraduate Topics in Computer Science.

[16]  J. H. Hubbell,et al.  Tables of X-Ray Mass Attenuation Coefficients and Mass Energy-Absorption Coefficients 1 keV to 20 MeV for Elements Z = 1 to 92 and 48 Additional Substances of Dosimetric Interest , 1995 .

[17]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[18]  Domingo Mery,et al.  Active X-ray testing of complex objects , 2012 .

[19]  K Wells,et al.  A review of X-ray explosives detection techniques for checked baggage. , 2012, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[20]  Horst Bischof,et al.  Comparison of Multiple View Strategies to Reduce False Positives in Breast Imaging , 2008, Digital Mammography / IWDM.

[21]  J. Als-Nielsen,et al.  Elements of Modern X-ray Physics: Als-Nielsen/Elements , 2011 .

[22]  Kurt Konolige,et al.  FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping , 2008, IEEE Transactions on Robotics.

[23]  J. Dinten,et al.  Dual-Energy X-Ray Imaging: Benefits and Limits , 2007 .

[24]  G. Agoston The Concept of Color , 1987 .

[25]  Reinhard Klette,et al.  Concise Computer Vision: An Introduction into Theory and Algorithms , 2014 .

[26]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[27]  Matthias Purschke,et al.  IQI-Sensitivity and Applications of Flat Panel Detectors and X-Ray Image Intensifiers ─ A Comparison , 2002 .

[28]  Domingo Mery Exploiting multiple view geometry in X-ray testing: Part I, theory , 2003 .

[29]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[30]  J. Rowlands,et al.  The physics of computed radiography. , 2002, Physics in medicine and biology.

[31]  Silvio Savarese,et al.  Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[32]  B. Cullity,et al.  Elements of X-ray diffraction , 1957 .

[33]  Domingo Mery,et al.  Visual inspection of glass bottlenecks by multiple-view analysis , 2010, Int. J. Comput. Integr. Manuf..

[34]  Технология Springer Science+Business Media , 2013 .

[35]  Dieter Filbert,et al.  Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence , 2002, IEEE Trans. Robotics Autom..

[36]  F. Dammann,et al.  Bildverarbeitung in der Radiologie , 2002 .

[37]  Maneesha Singh,et al.  Explosives detection systems (EDS) for aviation security , 2003, Signal Process..

[38]  Domingo Mery,et al.  A review of methods for automated recognition of casting defects , 2002 .

[39]  Muhammet Bastan,et al.  Visual Words on Baggage X-Ray Images , 2011, CAIP.

[40]  Rajiv Gupta,et al.  High precision X-ray stereo for automated 3D CAD-based inspection , 1998, IEEE Trans. Robotics Autom..

[41]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[42]  Olivier D. Faugeras,et al.  The geometry of multiple images - the laws that govern the formation of multiple images of a scene and some of their applications , 2001 .