Computer Vision Metrics: Textbook Edition by Scott KrigComputer Vision Metrics: Textbook Edition by Scott Krig

Computer Vision Metrics: Textbook Edition

byScott Krig

Hardcover | October 4, 2016

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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.
Scott Krig is a pioneer in computer imaging, computer vision, and graphics visualization. He founded Krig Research in 1988, providing the world's first image and vision systems based on high-performance engineering workstations, super-computers, and dedicated imaging hardware, serving customers worldwide in 25 countries. Scott has prov...
Title:Computer Vision Metrics: Textbook EditionFormat:HardcoverDimensions:637 pagesPublished:October 4, 2016Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319337610

ISBN - 13:9783319337616


Table of Contents

Image Capture and Representation.- Image Re-processing.- Global and Regional Features.- Local Feature Design Concepts.- Taxonomy of Feature Description Attributes.- Interest Point Detector and Feature Descriptor Survey.- Ground Truth Data, Content, Metrics, and Analysis.- Vision Pipeline and Optimizations.- Feature Learning Architecture Taxonomy and Neuroscience Background.- Feature Learning and Deep Learning Architecture Survey.