Computer Vision Metrics

Beschreibung

This second edition provides a comprehensive history and state-of-the-art survey for fundamental computer vision methods. Expanded and updated, this book features over 300 new references, totaling over 800 in all, as well as learning assignments at the end of each chapter to help students and researchers dig deeper into key topics. This survey covers everything from imaging devices, computational imaging, interest point detectors, local 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 book includes useful analysis to provide intuition into the goals of various methods, why they work, and how they may be optimized. This is not a how-to book with source code examples, but rather a survey and taxonomy intended as a reference tool for researchers and engineers, complimenting the many fine hand-on resources and open source projects such as OpenCV and other imaging and deep learning tools.
Provides the most complete survey of computer vision feature description methods including local, regional, global, basis, and feature learning via deep learning and neural networksOffers learning assignments at the end of each chapter for student or instructor useIncludes techniques for optimizing computer vision algorithm performance such as SW and HW architecture considerations

Autor*in

Scott Krig

Themen in »Computer Vision Metrics«

3D reconstruction CNN Computational imaging Computational neuroscience Computer vision Convolutional neural networks DNN Deep learning Deep neural networks Feature descriptors Feature learning Image processing Neural networks

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