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008 150724s2015 gw | s |||| 0|eng d
020 _a9783319207476
_z978-3-319-20747-6
024 7 _a10.1007/978-3-319-20747-6
_2doi
040 _aTR-AnTOB
_beng
_cTR-AnTOB
_erda
050 4 _aTA1637-1638
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM012000
_2bisacsh
072 7 _aUYT
_2thema
072 7 _aUYQV
_2thema006.6
_223006.37
_223
100 1 _aMery, Domingo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aComputer Vision for X-Ray Testing :
_bImaging, Systems, Image Databases, and Algorithms /
_cby Domingo Mery.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aX-ray Testing -- Images for X-ray Testing -- Geometry in X-ray Testing -- X-ray Image Processing -- X-ray Image Representation -- Classification in X-ray Testing -- Simulation in X-ray Testing -- Applications in X-ray Testing -- Appendix A: GDXray Details -- Appendix B: XVIS Toolbox -- Quick Reference.
520 _aThis accessible textbook presents an introduction to computer vision algorithms for industrially-relevant applications of X-ray testing. Covering complex topics in an easy-to-understand way, without requiring any prior knowledge in the field, the book provides a concise review of the key methodologies in computer vision for solving important problems in industrial radiology. The theoretical coverage is supported by numerous examples, each of which can be tested and evaluated by the reader using a freely-available Matlab toolbox and X-ray image database. Topics and features: Introduces the mathematical background for monocular and multiple view geometry, which is commonly used in X-ray computer vision systems Describes the main techniques for image processing used in X-ray testing, including image filtering, edge detection, image segmentation and image restoration 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, and techniques for estimating the accuracy of a classifier 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 This classroom-tested and hands-on guide is ideal for graduate and advanced undergraduate students interested in the practical application of image processing, pattern recognition and computer vision techniques for non-destructive quality testing and security inspection.
650 0 _aComputer vision.
650 0 _aSystem safety.
650 0 _aOptical pattern recognition.
650 1 4 _aImage Processing and Computer Vision.
_0http://scigraph.springernature.com/things/product-market-codes/I22021
650 2 4 _aQuality Control, Reliability, Safety and Risk.
_0http://scigraph.springernature.com/things/product-market-codes/T22032
650 2 4 _aPattern Recognition.
_0http://scigraph.springernature.com/things/product-market-codes/I2203X
710 2 _aSpringerLink (Online service)
856 4 0 _3Springer eBooks
_zOnline access link to the resource
_uhttps://doi.org/10.1007/978-3-319-20747-6
942 _2lcc
_cEBK
041 _aeng