Sinossi
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
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Foundations of Computer Vision (Adaptive Computation and Machine Learning series)
Editore:
The MIT Press (edition ), 2024
ISBN 10: 0262048973
ISBN 13: 9780262048972
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Hardcover. Condizione: new. Hardcover. 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.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 learningAccessible 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 "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 multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780262048972
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