000			02514 a2200217 4500
005			20251010093654.0
008			251010b |||||||| |||| 00| 0 eng d
020			_a9783030343712
082			_a006.37 _bSzeC
100			_aSzeliski, Richard
245			_aComputer Vision : _bAlgorithms and Applications / _cRichard Szeliski
250			_a2nd ed.
260			_bSpringer : _aNature Switzerland , _c©2022.
300			_axxii,925p.
440			_aText in computer Science :
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.
650			_aDeep Learning
650			_aMotion Estimation
650			_aComutational Photography
942			_cBK
999			_c6638 _d6638