| 000 | 03756nam a22005415i 4500 | ||
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| 001 | 978-3-031-61712-6 | ||
| 003 | DE-He213 | ||
| 005 | 20251030112647.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240724s2024 sz | s |||| 0|eng d | ||
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_a9783031617126 _9978-3-031-61712-6 |
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| 024 | 7 |
_a10.1007/978-3-031-61712-6 _2doi |
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| 050 | 4 | _aQA71-90 | |
| 072 | 7 |
_aPBKS _2bicssc |
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| 072 | 7 |
_aMAT041000 _2bisacsh |
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| 072 | 7 |
_aPBKS _2thema |
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| 100 | 1 |
_aAdouani, Ines. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _981194 |
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| 245 | 1 | 0 |
_aRegression and Fitting on Manifold-valued Data _h[electronic resource] / _cby Ines Adouani, Chafik Samir. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2024. |
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| 300 |
_aVII, 181 p. 47 illus., 45 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aIntroduction -- Spline Interpolation and Fitting in Rπ -- Spline Interpolation on the Sphere Sπ -- Spline Interpolation on the Special Orthogonal Group πΊπΆ(π) -- Spline Interpolation on Stiefel and Grassmann manifolds -- Spline Interpolation on the Manifold of Probability Measures -- Spline Interpolation on the Manifold of Probability Density Functions -- Spline Interpolation on Shape Space -- Spline Interpolation on Other Riemannian Manifolds. | |
| 520 | _aThis book introduces in a constructive manner a general framework for regression and fitting methods for many applications and tasks involving data on manifolds. The methodology has important and varied applications in machine learning, medicine, robotics, biology, computer vision, human biometrics, nanomanufacturing, signal processing, and image analysis, etc. The first chapter gives motivation examples, a wide range of applications, raised challenges, raised challenges, and some concerns. The second chapter gives a comprehensive exploration and step-by-step illustrations for Euclidean cases. Another dedicated chapter covers the geometric tools needed for each manifold and provides expressions and key notions for any application for manifold-valued data. All loss functions and optimization methods are given as algorithms and can be easily implemented. In particular, many popular manifolds are considered with derived and specific formulations. The same philosophy is used in all chapters and all novelties are illustrated with intuitive examples. Additionally, each chapter includes simulations and experiments on real-world problems for understanding and potential extensions for a wide range of applications. | ||
| 650 | 0 |
_aMathematics _xData processing. _965715 |
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| 650 | 0 |
_aImage processing _xDigital techniques. _964228 |
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| 650 | 0 | _aComputer vision. | |
| 650 | 0 |
_aEngineering mathematics. _964145 |
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| 650 | 1 | 4 |
_aComputational Mathematics and Numerical Analysis. _966073 |
| 650 | 2 | 4 |
_aComputer Imaging, Vision, Pattern Recognition and Graphics. _965550 |
| 650 | 2 | 4 |
_aEngineering Mathematics. _964145 |
| 700 | 1 |
_aSamir, Chafik. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _981195 |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031617119 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031617133 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031617140 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-61712-6 |
| 912 | _aZDB-2-SMA | ||
| 912 | _aZDB-2-SXMS | ||
| 999 |
_c61873 _d61873 |
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