关于此电子书
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.
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
评分和评价
5.0
1 条评价
作者简介
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.
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.
为此电子书评分
欢迎向我们提供反馈意见。
如何阅读
智能手机和平板电脑
笔记本电脑和台式机
您可以使用计算机的网络浏览器聆听您在 Google Play 购买的有声读物。
电子阅读器和其他设备
如果要在 Kobo 电子阅读器等电子墨水屏设备上阅读，您需要下载一个文件，并将其传输到相应设备上。若要将文件传输到受支持的电子阅读器上，请按帮助中心内的详细说明操作。
