國立虎尾科技大學 |

Computer Vision Metrics = Textbook Edition /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computer Vision Metrics/ by Scott Krig.
其他題名:	Textbook Edition /
作者:	Krig, Scott.
面頁冊數:	XVIII, 637 p. 331 illus., 139 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	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 /
LDR:02920nam a22003975i 4500 001 977141
003 DE-He213
005 20200701140621.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319337623 $9 978-3-319-33762-3
024 7 $a 10.1007/978-3-319-33762-3 $2 doi
035 $a 978-3-319-33762-3
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Krig, Scott. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1113458
245 1 0 $a Computer Vision Metrics $h [electronic resource] : $b Textbook Edition / $c by Scott Krig.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XVIII, 637 p. 331 illus., 139 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 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.
650 0 $a Artificial intelligence. $3 559380
650 0 $a Data mining. $3 528622
650 0 $a Signal processing. $3 561459
650 0 $a Image processing. $3 557495
650 0 $a Speech processing systems. $3 564428
650 0 $a Computational intelligence. $3 568984
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Computational Intelligence. $3 768837
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319337616
776 0 8 $i Printed edition: $z 9783319337630
776 0 8 $i Printed edition: $z 9783319815954
856 4 0 $u https://doi.org/10.1007/978-3-319-33762-3
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)