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Computer Vision: Algorithms and Applications
Computer 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
Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized coursesIncorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented realityPresents 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.
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
Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized coursesIncorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented realityPresents 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.
947 pages, Kindle Edition
First published October 2, 2010
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Displaying 1 - 4 of 4 reviews
August 11, 2024
Comprehensive overview of CV, complete with the mathematical foundations and optical physics. Dense, but it’s one of my biggest reference texts.
April 15, 2013
Even though I am fond of cutting-edge textbooks, I don't quite recommend this to read for FUN... Instead, this book should belong to the category of typical REFERENCE BOOKS since it's comprehensive and inevitably dull...
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June 19, 2015Location: GG6 IRC
Accession No: DL027195
Accession No: DL027195
Displaying 1 - 4 of 4 reviews