東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Big visual data analysis = scene cla...

Chen, Chen.

Linked to FindBook

Google Book

Amazon

博客來

Big visual data analysis = scene classification and geometric labeling /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big visual data analysis/ by Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo.
Reminder of title:	scene classification and geometric labeling /
Author:	Chen, Chen.
other author:	Ren, Yuzhuo.
Published:	Singapore :Springer Singapore : : 2016.,
Description:	x, 122 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Scene Understanding Datasets -- Indoor/Outdoor classiﬁcation with Multiple Experts -- Outdoor Scene Classiﬁcation Using Labeled Segments -- Global-Attributes Assisted Outdoor Scene Geometric Labeling -- Conclusion and Future Work.
Contained By:	Springer eBooks
Subject:	Computer vision. -
Online resource:	http://dx.doi.org/10.1007/978-981-10-0631-9
ISBN:	9789811006319$q(electronic bk.)

Big visual data analysis = scene classification and geometric labeling /
Chen, Chen.

Big visual data analysisscene classification and geometric labeling /[electronic resource] :by Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo. - Singapore :Springer Singapore :2016. - x, 122 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

Introduction -- Scene Understanding Datasets -- Indoor/Outdoor classiﬁcation with Multiple Experts -- Outdoor Scene Classiﬁcation Using Labeled Segments -- Global-Attributes Assisted Outdoor Scene Geometric Labeling -- Conclusion and Future Work.

This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.

ISBN: 9789811006319$q(electronic bk.)

Standard No.: 10.1007/978-981-10-0631-9doiSubjects--Topical Terms:

540671
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Big visual data analysis = scene classification and geometric labeling /
LDR:02226nmm a2200349 a 4500 001 2030919
003 DE-He213
005 20160823165831.0
006 m d
007 cr nn 008maaau
008 160908s2016 si s 0 eng d
020 $a 9789811006319$q(electronic bk.)
020 $a 9789811006296$q(paper)
024 7 $a 10.1007/978-981-10-0631-9 $2 doi
035 $a 978-981-10-0631-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1634
072 7 $a TTBM $2 bicssc
072 7 $a UYS $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a COM073000 $2 bisacsh
082 0 4 $a 006.37 $2 23
090 $a TA1634 $b .C518 2016
100 1 $a Chen, Chen. $3 1267258
245 1 0 $a Big visual data analysis $h [electronic resource] : $b scene classification and geometric labeling / $c by Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2016.
300 $a x, 122 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in electrical and computer engineering, $x 2191-8112
505 0 $a Introduction -- Scene Understanding Datasets -- Indoor/Outdoor classiﬁcation with Multiple Experts -- Outdoor Scene Classiﬁcation Using Labeled Segments -- Global-Attributes Assisted Outdoor Scene Geometric Labeling -- Conclusion and Future Work.
520 $a This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.
650 0 $a Computer vision. $3 540671
650 0 $a Image processing $x Digital techniques. $3 532550
650 1 4 $a Engineering. $3 586835
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Visualization. $3 586179
700 1 $a Ren, Yuzhuo. $3 2182986
700 1 $a Kuo, C.-C. Jay. $3 923819
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in electrical and computer engineering. $3 1565565
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-0631-9
950 $a Engineering (Springer-11647)