ISBN: 9780262048972
Antonio Torralba(Author); Phillip Isola(Author),
Foundations of Computer Vision (Adaptive Computation and Machine Learning series) [Hardcover] Torralba, Antonio; Isola, Phillip and Freeman, William T.
Foundations of Computer Vision (Adaptive Computation and Machine Learning series) [Hardcover] Torralba, Antonio; Isola, Phillip and Freeman, William T.
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Author
Antonio Torralba(Author); Phillip Isola(Author),
Publisher
The MIT Press
Books
1 volume
ISBN 13
9780262048972
About this edition
An accessible, authoritative, and up-to-date computer vision textbook offering a comprehensive introduction to the foundations of the field that incorporates the latest deep learning advances.Machine learning has revolutionized computer vision, but the methods of today have deep roots in the history of the field. Providing a much-needed modern treatment, this accessible and up-to-date textbook comprehensively introduces the foundations of computer vision while incorporating the latest deep learning advances. Taking a holistic approach that goes beyond machine learning, it addresses fundamental issues in the task of vision and the relationship of machine vision to human perception. Foundations of Computer Vision covers topics not standard in other texts, including transformers, diffusion models, statistical image models, issues of fairness and ethics, and the research process. To emphasize intuitive learning, concepts are presented in short, lucid chapters alongside extensive illustrations, questions, and examples. Written by leaders in the field and honed by a decade of classroom experience, this engaging and highly teachable book offers an essential next-generation view of computer vision. Up-to-date treatment integrates classic computer vision and deep learning Accessible approach emphasizes fundamentals and assumes little background knowledgeStudent-friendly presentation features extensive examples and imagesProven in the classroomInstructor resources include slides, solutions, and source code
More details
Publisher: The MIT Press
Language: English
Print length: 840 pages
Binding: Print length
Dimensions: 8.27 x 1.42 x 9.29 inches
Item weight: 3.24 pounds
Best Sellers Rank (Amazon): 110871
Language: English
Print length: 840 pages
Binding: Print length
Dimensions: 8.27 x 1.42 x 9.29 inches
Item weight: 3.24 pounds
Best Sellers Rank (Amazon): 110871
Shipping and returns
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If the returned book is damaged or missing components, the refund may be denied. If the book arrives damaged (e.g., due to shipping issues), a full refund will be issued. For returns due to non-quality issues (e.g., buyer’s change of mind), the customer must cover return shipping costs.
1. Return Window
- Eligible for return within 30 days of delivery.
69. Return Conditions
- The book must be brand new (unused, unmarked, and undamaged).
Important Notes:
If the returned book is damaged or missing components, the refund may be denied. If the book arrives damaged (e.g., due to shipping issues), a full refund will be issued. For returns due to non-quality issues (e.g., buyer’s change of mind), the customer must cover return shipping costs.
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The MIT Press
Foundations of Computer Vision (Adaptive Computation and Machine Learning series) [Hardcover] Torralba, Antonio; Isola, Phillip and Freeman, William T.
What's inside the box
Book 1
Foundations of Computer Vision
ISBN 9780262048972
About this collection
Master the essentials of modern visual AI with Foundations of Computer Vision.
Follow leading industry experts as they bridge classical computer vision principles with modern deep learning advances like transformers and diffusion models.
Includes lucid short chapters, extensive visual illustrations, real-world examples, and key discussions on ethics and fairness.
Add this essential, comprehensive computer vision textbook to your technical library today.
Over 5,000 pages of gripping storytelling in a stylish, display-worthy format.
Perfect for longtime fans or readers new to the series — the ultimate set for fantasy lovers or collectors looking to complete their shelves.
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