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Computer Vision Metrics = Textbook Edition /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computer Vision Metrics/ by Scott Krig.
Reminder of title:	Textbook Edition /
Author:	Krig, Scott.
Description:	XVIII, 637 p. 331 illus., 139 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-33762-3
ISBN:	9783319337623

Computer Vision Metrics = Textbook Edition /
Krig, Scott.

Computer Vision MetricsTextbook Edition /[electronic resource] :by Scott Krig. - 1st ed. 2016. - XVIII, 637 p. 331 illus., 139 illus. in color.online resource.

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. .

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.

ISBN: 9783319337623

Standard No.: 10.1007/978-3-319-33762-3doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Computer Vision Metrics = Textbook Edition /
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