Foundations of Computer Vision (Hardcover)

Antonio Torralba

ISBN 10: 0262048973 / ISBN 13: 9780262048972

Published by MIT Press Ltd, 2024

About this Item

Hardcover. Plt;bPgt;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.Plt;/bPgt;Plt;bPgt;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.Plt;/bPgt;Plt;brPgt;Plt;brPgt;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. Plt;iPgt;Foundations of Computer VisionPlt;/iPgt; 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. Plt;brPgt;Plt;brPgt;Plt;ulPgt;Plt;liPgt;Up-to-date treatment integrates classic computer vision and deep learning Plt;/liPgt;Plt;liPgt;Accessible approach emphasizes fundamentals and assumes little background knowledgePlt;/liPgt;Plt;liPgt;Student-friendly presentation features extensive examples and imagesPlt;/liPgt;Plt;liPgt;Proven in the classroomPlt;/liPgt;Plt;liPgt;Instructor resources include slides, solutions, and source codePlt;/liPgt;Plt;/ulPgt; "An up-to-date computer vision textbook incorporating the latest deep learning advances that have revolutionized the field over the last decade"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9780262048972

Bibliographic Details

• Title: Foundations of Computer Vision (Hardcover)
• Publisher: MIT Press Ltd
• Publication Date: 2024
• Language: English
• ISBN 10: 0262048973
• ISBN 13: 9780262048972
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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 knowledge
• Student-friendly presentation features extensive examples and images
• Proven in the classroom
• Instructor resources include slides, solutions, and source code

About the Author

Antonio Torralba is Professor and Head of the AI+D faculty at the Department of Electrical Engineering and Computer Science at MIT, where he is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). 
Phillip Isola is Associate Professor of Electrical Engineering and Computer Science at MIT, where he is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). 
William T. Freeman is Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science at MIT, where he is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also a research manager at Google Research in Cambridge, Massachusetts.