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Geometric Algebra Applications Vol. I: Computer Vision, Graphics and Neurocomputing

Autor Eduardo Bayro-Corrochano
en Limba Engleză Hardback – 5 iul 2018
The 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.
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Specificații

ISBN-13: 9783319748283
ISBN-10: 3319748289
Pagini: 400
Ilustrații: XXXIII, 742 p. 262 illus., 151 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 1.26 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Cuprins

Fundamentals 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.

Textul de pe ultima copertă

The 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.

Caracteristici

Offers in a compact and complete way the theory and methods to apply Geometric Algebra in computer vision, graphics and machine learning Introduces the basics of geometric algebra to specialists and non- specialists in a gentle and comprehensive manner using examples and abundant figures and simulation results Step by step using examples encourages readers to learn how to model, design algorithms for modern applications in the areas of computer vision, graphics and machine learning