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020			_a9783319748306
024	7		_a10.1007/978-3-319-74830-6 _2doi
040			_aES-MaUEC _bspa _dES-MaUEC
050		4	_aQA448.D38 _b2019 EB
100	1		_aBayro Corrochano, Eduardo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671296
245	1	0	_aGeometric Algebra Applications Vol. I : _bComputer Vision, Graphics and Neurocomputing _cby Eduardo Bayro-Corrochano.
264		1	_aCham _bSpringer International Publishing _bImprint: Springer _c2019.
300			_a1 recurso en línea (XXXIII, 742 páginas) _b262 ilustraciones,151 ilustraciones a color
347			_atext file _bPDF
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aFundamentals of Geometric Algebra -- Euclidean, Pseudo-Euclidean Geometric Algebra, Incidence Algebra and Conformal Geometric Algebras -- Geometric Computing for Image Processing, Computer Vision, and Neural Computing -- Machine Learning -- Applications of Geometric Algebra in Image Processing, Graphics and Computer Vision -- Applications of GA in Machine Learning -- Appendix.
520	3		_aThe goal of the Volume I Geometric Algebra for Computer Vision, Graphics and Neural Computing is to present a unified mathematical treatment of diverse problems in the general domain of artificial intelligence and associated fields using Clifford, or geometric, algebra. Geometric algebra provides a rich and general mathematical framework for Geometric Cybernetics in order to develop solutions, concepts and computer algorithms without losing geometric insight of the problem in question. Current mathematical subjects can be treated in an unified manner without abandoning the mathematical system of geometric algebra for instance: multilinear algebra, projective and affine geometry, calculus on manifolds, Riemann geometry, the representation of Lie algebras and Lie groups using bivector algebras and conformal geometry. By treating a wide spectrum of problems in a common language, this Volume I offers both new insights and new solutions that should be useful to scientists, and engineers working in different areas related with the development and building of intelligent machines. Each chapter is written in accessible terms accompanied by numerous examples, figures and a complementary appendix on Clifford algebras, all to clarify the theory and the crucial aspects of the application of geometric algebra to problems in graphics engineering, image processing, pattern recognition, computer vision, machine learning, neural computing and cognitive systems.
650		7	_aGeomatría _xProceso de datos _2embne
776	0	8	_iPrinted edition: _z9783319748283
776	0	8	_iPrinted edition: _z9783319748290
776	0	8	_iPrinted edition: _z9783030090852
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-74830-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Robotics
998			_aSI _a_alco _a_vill _b11/2019 _cm _dz _ea _feng _ggw _h0