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Computer vision [2nd ed.] : algorithms and applications

By: Szeliski, Richard.
Series: Texts in computer science. / edited by David Gries and Orit Hazzan.Publisher: Switzerland Springer Nature 2022Edition: 2nd ed.Description: xxii, 925p.ISBN: 9783030343712.Subject(s): Computer vision | Image processing | Image processing -- MathematicsDDC classification: 006.37 | Sz25c2 Summary: 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.
List(s) this item appears in: New arrival July 18 to 24, 2022
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Item type Current location Collection Call number Status Date due Barcode Item holds
Books Books PK Kelkar Library, IIT Kanpur
General Stacks 006.37 Sz25c2 (Browse shelf) Checked out to ADITYA SHARMA (S20005400) 02/03/2024 A185871
Total holds: 0

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.

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