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020			_a9783319229577
040			_aES-MaUEC
050		4	_aTK5102.9 _bR546 2016
082	0	4	_a621.382
245	1	0	_aRiemannian Computing in Computer Vision _cedited by Pavan K Turaga, Anuj Srivastava
250			_a1st ed.
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (VI, 391 páginas) _b88 ilustraciones, 66 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aWelcome to Riemannian Computing in Computer Vision -- Recursive Computation of the Fréchet Mean on Non-Positively Curved Riemannian Manifolds with Applications -- Kernels on Riemannian Manifolds -- Canonical Correlation Analysis on SPD(n) manifolds -- Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds -- Robust Estimation for Computer Vision using Grassmann Manifolds -- Motion Averaging in 3D Reconstruction Problems -- Lie-Theoretic Multi-Robot Localization -- CovarianceWeighted Procrustes Analysis -- Elastic Shape Analysis of Functions, Curves and Trajectories -- Why Use Sobolev Metrics on the Space of Curves -- Elastic Shape Analysis of Surfaces and Images -- Designing a Boosted Classifier on Riemannian Manifolds -- A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision -- Domain Adaptation Using the Grassmann Manifold -- Coordinate Coding on the Riemannian Manifold of Symmetric Positive Definite Matrices for Image Classification -- Summarization and Search over Geometric Spaces.
520	3		_aThis book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours). � ·�������� Illustrates Riemannian computing theory on applications in computer vision, machine learning, and robotics ·�������� Emphasis on algorithmic advances that will allow re-application in other contexts ·�������� Written by leading researchers in computer vision and Riemannian computing, from universities and industry.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, asignarmaterias , EBSPRINGER
650	0	4	_9669495 _aProceso de imágenes _0
650		7	_aMatemáticas aplicadas _0comprobar BNE19920885281 _2embne _9145503
700	1		_aTuraga, Pavan K. _eeditor literario _997415 _0Local
700	1		_aSrivastava, Anuj. _eeditor literario _997416 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-22957-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319229577
907			_a.b1294158x _b10-10-17 _c21-11-16
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945			_aTK5102.9 R546 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11585985 _z06-04-17
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