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020			_a9783030343729
024	7		_a10.1007/978-3-030-34372-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2022 EB
100	1		_aSzeliski, Richard, _eautor _9683104 _d1958-
245	1	0	_aComputer Vision : _bAlgorithms and Applications _cby Richard Szeliski
250			_aSecond edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (XXII, 925 páginas) _b518 ilustraciones, 144 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aTexts in Computer Science _x1868-095X
505	0		_a1 Introduction -- 2 Image Formation -- 3 Image Processing -- 4 Model Fitting and Optimization -- 5 Deep Learning -- 6 Recognition -- 7 Feature Detection and Matching -- 8 Image Alignment and Stitching -- 9 Motion Estimation -- 10 Computational Photography -- 11 Structure from Motion and SLAM -- 12 Depth Estimation -- 13 3D Reconstruction -- 14 Image-Based Rendering -- 15 Conclusion -- Appendix A: Linear Algebra and Numerical Techniques -- Appendix B: Bayesian Modeling and Inference -- Appendix C: Supplementary Material.
520			_aComputer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos. More than just a source of "recipes," this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles. Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision. About the Author Dr. Richard Szeliski has more than 40 years' experience in computer vision research, most recently at Facebook and Microsoft Research, where he led the Computational Photography and Interactive Visual Media groups. He is currently an Affiliate Professor at the University of Washington where he co-developed (with Steve Seitz) the widely adopted computer vision curriculum on which this book is based.
988			_aSpringer_Computer_2022
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9151819 _aAlgoritmos computacionales
773	0		_tSpringer Nature eBook
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-34372-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2022 _dz _eu _zSI