000 03445nam a22003735i 4500
001 299491
003 MX-SnUAN
005 20160429155551.0
007 cr nn 008mamaa
008 150903s2009 gw | o |||| 0|eng d
020 _a9783642006838
_99783642006838
024 7 _a10.1007/9783642006838
_2doi
035 _avtls000352939
039 9 _a201509030925
_bVLOAD
_c201405060313
_dVLOAD
_y201402180932
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTA401-492
100 1 _aLouban, Roman.
_eautor
_9336445
245 1 0 _aImage Processing of Edge and Surface Defects :
_bTheoretical Basis of Adaptive Algorithms with Numerous Practical Applications /
_cby Roman Louban.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2009.
300 _axI, 168 páginas 128 ilustraciones, 8 ilustraciones en color.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aSpringer Series in Materials Science,
_x0933-033X ;
_v123
500 _aSpringer eBooks
505 0 _aEdge Detection -- Defect Detection on an Edge -- Defect Detection on an Inhomogeneous High-Contrast Surface -- Defect Detection on an Inhomogeneous Structured Surface -- Defect Detection in Turbo Mode -- Adaptive Edge and Defect Detection as a basis for Automated Lumber Classification and Optimisation -- Object Detection on Images Captured Using a Special Equipment -- Before an Image Processing System is Used.
520 _aThe edge and surface inspection is one of the most important and most challenging tasks in quality assessment in industrial production. Typical defects are cracks, inclusions, pores, surface flakings, partial or complete tears of material surface and s.o. These defects can occur through defective source material or through extreme strain during machining process. Detection of defects on a materialc surface can be complicated due to extremely varying degrees of material brightness or due to shadow areas, caused by the folding of the surface. Furthermore, impurities or surface discolourations can lead to artefacts that can be detected as pseudo-defects. The brightness conditions on the edge of material defects are interpreted as a Gauss distribution of a radiation and used for a physical model. Basing on this model, an essentially new set of adaptive edge-based algorithms was developed. Using these methods, different types of defects can be detected, without the measurements being dependent on local or global brightness conditions of the image taken. The new adaptive edge-based algorithms allow a defect detection on different materials, like metal, ceramics, plastics and stone. These methods make it possible to explicitly detect all kinds of different defects independently of their size, form and position and of the surface to be inspected. The adaptive edge-based methods provide a very wide spectrum of applications.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783642006821
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-00683-8
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c299491
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