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Computer Vision
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Computer Vision : Algorithms and Applications

Second Edition 2022

by Richard Szeliski

Book Details

Format Hardback or Cased Book
Book Series Texts in Computer Science
ISBN-10 3030343715
ISBN-13 9783030343712
Edition Second Edition 2022
Publisher Springer Nature Switzerland AG
Imprint Springer Nature Switzerland AG
Country of Manufacture CH
Country of Publication GB
Publication Date Jan 5th, 2022
Print length 925 Pages
Weight 2,780 grams
Dimensions 28.70 x 22.10 x 6.30 cms
Product Classification: Materials science Imaging systems & technology Machine learning Computer vision Image processing
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Computer 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 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 projectsIncludes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decadeProvides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and softwareSuitable 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.

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