000			03367nam a22004215i 4500
999			_c387610 _d387610
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003			ES-MaUEC
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007			cr nn 008mamaa
008			230325s2017 sz | s |||| 0|eng d
020			_a9783031025938
024	7		_a10.1007/978-3-031-02593-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA448.D38 _b2017 EB
100	1		_aPatanè, Giuseppe, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686409 _d1974-
245	1	3	_aAn Introduction to Laplacian Spectral Distances and Kernels : _bTheory, Computation, and Applications _cby Giuseppe Patanè
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XX, 120 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Visual Computing: Computer Graphics Animation Computational Photography and Imaging _x2469-4223
505	0		_aList of Figures -- List of Tables -- Preface -- Acknowledgments -- Laplace Beltrami Operator -- Heat and Wave Equations -- Laplacian Spectral Distances -- Discrete Spectral Distances -- Applications -- Conclusions -- Bibliography -- Author's Biography.
520			_aIn geometry processing and shape analysis, several applications have been addressed through the properties of the Laplacian spectral kernels and distances, such as commute time, biharmonic, diffusion, and wave distances. Within this context, this book is intended to provide a common background on the definition and computation of the Laplacian spectral kernels and distances for geometry processing and shape analysis. To this end, we define a unified representation of the isotropic and anisotropic discrete Laplacian operator on surfaces and volumes; then, we introduce the associated differential equations, i.e., the harmonic equation, the Laplacian eigenproblem, and the heat equation. Filtering the Laplacian spectrum, we introduce the Laplacian spectral distances, which generalize the commute-time, biharmonic, diffusion, and wave distances, and their discretization in terms of the Laplacian spectrum. As main applications, we discuss the design of smooth functions and the Laplacian smoothing of noisy scalar functions. All the reviewed numerical schemes are discussed and compared in terms of robustness, approximation accuracy, and computational cost, thus supporting the reader in the selection of the most appropriate with respect to shape representation, computational resources, and target application.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9138192 _aGeometría _xProceso de datos
650		7	_2embne _9141143 _aGráficos de ordenador _xMatemáticas
650		7	_2embne _9687571 _aFunciones armónicas
776	0	8	_iPrinted edition: _z9783031014659
776	0	8	_iPrinted edition: _z9783031037214
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02593-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI