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020 _a9783319484938
024 7 _a10.1007/978-3-319-48493-8
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aTA1634
_b2016 EB
100 1 _aKanatani, Kenʼichi,
_9101824
_d1947-
245 1 0 _aGuide to 3D Vision Computation :
_bGeometric Analysis and Implementation
_cby Kenichi Kanatani, Yasuyuki Sugaya, Yasushi Kanazawa
264 1 _aCham
_bSpringer International Publishing
_c2016
300 _a1 recurso en línea (XI, 321 páginas)
_b54 ilustraciones, 10 ilustraciones en color
336 _aTexto (visual)
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
490 1 _aAdvances in Computer Vision and Pattern Recognition
_x2191-6586
505 0 _aIntroduction -- Part I: Fundamental Algorithms for Computer Vision -- Ellipse Fitting -- Fundamental Matrix Computation -- Triangulation -- 3D Reconstruction from Two Views -- Homography Computation -- Planar Triangulation -- 3D Reconstruction of a Plane -- Ellipse Analysis and 3D Computation of Circles -- Part II: Multiview 3D Reconstruction -- Multiview Triangulation -- Bundle Adjustment -- Self-calibration of Affine Cameras -- Self-calibration of Perspective Cameras -- Part III: Mathematical Foundation of Geometric Estimation -- Accuracy of Geometric Estimation -- Maximum Likelihood and Geometric Estimation -- Theoretical Accuracy Limit -- Solutions.
520 _aThis classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other textbooks on computer vision, this Guide to 3D Vision Computation takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system. The theoretical background is then briefly explained afterwards, highlighting how one can quickly and simply obtain the desired result without knowing the derivation of the mathematical detail. Topics and features: Reviews the fundamental algorithms underlying computer vision, and their implementation Describes the latest techniques for 3D reconstruction from multiple images Summarizes the mathematical theory behind statistical error analysis for general geometric estimation problems Offers examples of experimental results, enabling the reader to get a feeling of what can be done using each procedure Presents derivations and justifications as problems at the end of each chapter, with solutions supplied at the end of the book Explains the historical background for each topic in the supplemental notes at the end of each chapter Provides additional material at an associated website, include sample code for typical procedures to help readers implement the algorithms described in the book This accessible work will be of great value to students on introductory computer vision courses. Serving as both as a practical programming guidebook and a useful reference on mathematics for computer vision, it is suitable for practitioners seeking to implement computer vision algorithms as well as for theoreticians wishing to know the underlying mathematical detail.
988 _aEBOOK, EBSPRINGER
650 0 7 _aVisión por ordenador
_2embne
_9159793
650 0 7 _9669495
_aProceso de imágenes
_2embne
700 _aSugaya, Yasuyuki
_0Local
_9101825
700 1 _aKanazawa, Yasushi
_0Local
_9101826
830 0 _aAdvances in Computer Vision and Pattern Recognition
_x2191-6586
_0http://id.loc.gov/authorities/names/no2011103580
_9134066
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-48493-8zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901 _ai9783319484938
907 _a.b12982465
_b10-10-17
_c08-03-17
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998 _b06/2020
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