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001 u373966
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005 20160812084211.0
007 cr nn 008mamaa
008 100315s2010 gw | s |||| 0|eng d
020 _a9783642112164
_9978-3-642-11216-4
040 _cMX-MeUAM
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
082 0 4 _a621.382
_223
100 1 _aMitchell, H. B.
_eauthor.
245 1 0 _aImage Fusion
_h[recurso electrónico] :
_bTheories, Techniques and Applications /
_cby H. B. Mitchell.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _a200p. 52 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aImage Sensors -- I: Theories -- Common Representational Format -- Spatial Alignment -- Semantic Equivalence -- Radiometric Calibration -- Pixel Fusion -- II: Techniques -- Multi-resolution Analysis -- Image Sub-space Techniques -- Ensemble Learning -- Re-sampling Methods -- Image Thresholding -- Image Key Points -- Image Similarity Measures -- Vignetting, White Balancing and Automatic Gain Control Effects -- Color Image Spaces -- Markov Random Fields -- Image Quality -- III: Applications -- Pan-sharpening -- Ensemble Color Image Segmentation -- STAPLE: Simultaneous Truth and Performance Level Estimation -- Biometric Technologies.
520 _aThis textbook provides a comprehensive introduction to the theories, techniques and applications of image fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science. It should also be useful to practicing engineers who wish to learn the concepts of image fusion and use them in real-life applications. The book is intended to be self-contained. No previous knowledge of image fusion is assumed, although some familiarity with elementary image processing and the basic tools of linear algebra is recommended. The book may also be used as a supplementary text for a course on advanced image processing. Apart from two preliminary chapters, the book is divided into three parts. Part I deals with the conceptual theories and ideas which underlie image fusion. Particular emphasis is given to the concept of a common representational framework and includes detailed discussions on the techniques of image registration, radiometric calibration and semantic equivalence. Part II deals with a wide range of techniques and algorithms which are in common use in image fusion. Among the topics considered are: sub-space transformations, multi-resolution analysis, wavelets, ensemble learning, bagging, boosting, color spaces, image thresholding, Markov random fields, image similarity measures and the expectation-maximization algorithm. Together Parts I and II form an integrated and comprehensive overview of image fusion. Part III deals with applications. In it several real-life examples of image fusion are examined in detail, including panchromatic sharpening, ensemble color image segmentation and the Simultaneous Truth and Performance algorithm of Warfield et al. The book is accompanied by a webpage from which supplementary material may be obtained. This includes support for course instructors and links to relevant matlab code.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputer vision.
650 0 _aMathematics.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aApplications of Mathematics.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642112157
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-11216-4
596 _a19
942 _cLIBRO_ELEC
999 _c201846
_d201846