000 03196nam a22005535i 4500
001 978-1-4939-0533-1
003 DE-He213
005 20200421112226.0
007 cr nn 008mamaa
008 140314s2014 xxu| s |||| 0|eng d
020 _a9781493905331
_9978-1-4939-0533-1
024 7 _a10.1007/978-1-4939-0533-1
_2doi
050 4 _aTA1637-1638
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
_2bisacsh
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
100 1 _aMontegranario, Hebert.
_eauthor.
245 1 0 _aVariational Regularization of 3D Data
_h[electronic resource] :
_bExperiments with MATLAB� /
_cby Hebert Montegranario, Jairo Espinosa.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aX, 85 p. 21 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _a3D Data in Computer vision and technology -- Function Spaces and Reconstruction -- Variational methods -- Interpolation: From one to several variables -- Functionals and their physical interpretations -- Regularization and inverse theory -- 3D Interpolation and approximation -- Radial basis functions.
520 _aVariational Regularization of 3D Data provides an introduction to variational methods for data modelling and its application in computer vision. In this book, the authors identify interpolation as an inverse problem that can be solved by Tikhonov regularization. The proposed solutions are generalizations of one-dimensional splines, applicable to n-dimensional data and the central idea is that these splines can be obtained by regularization theory using a trade-off between the fidelity of the data and smoothness properties. As a foundation, the authors present a comprehensive guide to the necessary fundamentals of functional analysis and variational calculus, as well as splines. The implementation and numerical experiments are illustrated using MATLAB�. The book also includes the necessary theoretical background for approximation methods and some details of the computer implementation of the algorithms. A working knowledge of multivariable calculus and basic vector and matrix methods should serve as an adequate prerequisite.
650 0 _aComputer science.
650 0 _aComputer science
_xMathematics.
650 0 _aComputer simulation.
650 0 _aImage processing.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aMath Applications in Computer Science.
650 2 4 _aSimulation and Modeling.
700 1 _aEspinosa, Jairo.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781493905324
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4939-0533-1
912 _aZDB-2-SCS
942 _cEBK
999 _c57672
_d57672