| 书目名称 | Computer Vision Metrics | | 副标题 | Textbook Edition | | 编辑 | Scott Krig | | 视频video | | | 概述 | Provides the most complete survey of computer vision feature description methods including local, regional, global, and basis feature learning via deep learning and neural networks.Offers learning ass | | 图书封面 |  | | 描述 | Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. .To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized..The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.. | | 出版日期 | Textbook 20161st edition | | 关键词 | Computer vision; Deep learning; Feature learning; Feature descriptors; Image processing; Computational im | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-33762-3 | | isbn_softcover | 978-3-319-81595-4 | | isbn_ebook | 978-3-319-33762-3 | | copyright | Springer International Publishing Switzerland 2016 |
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发表于 2025-3-21 16:34:39
