Computer vision : algorithms and applications / Richard Szeliski.
Material type:
TextSeries: Texts in computer sciencePublisher: Cham, Switzerland : Springer, [2022] 2022Edition: 2nd editionDescription: xxii, 925 pages : illustrations (black and white, and colour) ; 28 cmContent type: text | still image Media type: unmediated Carrier type: volumeISBN: 9783030343712 (hbk.) :Subject(s): Computer vision | Computer algorithms | Image processing -- Mathematics | Computers and IT | Graphical & digital media applications | Materials science | Imaging systems & technology | Image processing | Machine learning | Computer vision | Electronics engineering | Artificial intelligence | Pattern recognitionDDC classification: 006.37 Summary: 'Computer Vision' explores the variety of techniques commonly used to analyse and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialised applications such as medical imaging and fun consumer-level tasks such as image editing and stitching. Computer Vision: Algorithms and Applicationsexplores 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.
| Item type | Current library | Home library | Shelving location | Class number | Status | Date due | Barcode | Item reservations | |
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Paul Hamlyn Library | Paul Hamlyn Library | Floor 1 | 006.37 SZE (Browse shelf(Opens below)) | Available | 06895247 |
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Previous edition: 2011.
Includes bibliographical references and index.
'Computer Vision' explores the variety of techniques commonly used to analyse and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialised applications such as medical imaging and fun consumer-level tasks such as image editing and stitching. Computer Vision: Algorithms and Applicationsexplores 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.
Description based on information supplied online (viewed on March 7, 2022).
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