Computer Vision : Algorithms and Applications / Richard Szeliski
Material type: TextLanguage: English Publication details: NewYork : Springer, 2022Description: 925 p. ; 28 cmISBN: 9783030343712Subject(s): Computer vision | Computer algorithms | Computer science | Khoa học máy tínhDDC classification: 006.37 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.Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
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Giáo trình |
Thư viện Trường Quốc tế - Cơ sở Hòa Lạc
Thư viện Trường Quốc tế - Đại học Quốc gia Hà Nội |
006.37 SZE 2022 | Checked out | 01/08/2024 | HL.1/00604 | |
Giáo trình |
Thư viện Trường Quốc tế - Cơ sở Trịnh Văn Bô
Thư viện Trường Quốc tế - Đại học Quốc gia Hà Nội |
006.37 SZE 2022 | Available | TVB.1/00229 |
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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