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Computer vision metrics = survey, taxonomy and analysis of computer vision, visual neuroscience, and deep learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computer vision metrics/ by Scott Krig.
Reminder of title:	survey, taxonomy and analysis of computer vision, visual neuroscience, and deep learning /
Author:	Krig, Scott.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xviii, 637 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Computer vision. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-33762-3
ISBN:	9783319337623$q(electronic bk.)

Computer vision metrics = survey, taxonomy and analysis of computer vision, visual neuroscience, and deep learning /
Krig, Scott.

Computer vision metricssurvey, taxonomy and analysis of computer vision, visual neuroscience, and deep learning /[electronic resource] :by Scott Krig. - Textbook ed. - Cham :Springer International Publishing :2016. - xviii, 637 p. :ill. (some col.), digital ;24 cm.

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. 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. 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 OpenCVand other imaging and deep learning tools.

ISBN: 9783319337623$q(electronic bk.)

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

540671
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Computer vision metrics = survey, taxonomy and analysis of computer vision, visual neuroscience, and deep learning /
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505 0 $a 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.
520 $a 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. 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. 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 OpenCVand other imaging and deep learning tools.
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