MARC details
| 000 -LEADER |
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| fixed length control field |
03882nam a22005415i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
978-3-319-04561-0 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20170628034112.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
140125s2014 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319045610 |
| -- |
978-3-319-04561-0 |
| 024 7# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.1007/978-3-319-04561-0 |
| Source of number or code |
doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TA1637-1638 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TA1637-1638 |
| 072 #7 - SUBJECT CATEGORY CODE |
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| Subject category code |
UYT |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
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| Subject category code |
UYQV |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
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| Subject category code |
COM012000 |
| Source |
bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE |
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| Subject category code |
COM016000 |
| Source |
bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.6 |
| Edition number |
23 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.37 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Wang, Jiang. |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Human Action Recognition with Depth Cameras |
| Medium |
[electronic resource] / |
| Statement of responsibility, etc |
by Jiang Wang, Zicheng Liu, Ying Wu. |
| 264 #1 - |
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| -- |
Cham : |
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Springer International Publishing : |
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Imprint: Springer, |
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2014. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
VIII, 59 p. 32 illus., 9 illus. in color. |
| Other physical details |
online resource. |
| 336 ## - |
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| -- |
text |
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txt |
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rdacontent |
| 337 ## - |
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computer |
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c |
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rdamedia |
| 338 ## - |
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| -- |
online resource |
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cr |
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rdacarrier |
| 347 ## - |
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| -- |
text file |
| -- |
PDF |
| -- |
rda |
| 490 1# - SERIES STATEMENT |
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| Series statement |
SpringerBriefs in Computer Science, |
| International Standard Serial Number |
2191-5768 |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Introduction -- Learning Actionlet Ensemble for 3D Human Action Recognition -- Random Occupancy Patterns -- Conclusion. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Action recognition is an enabling technology for many real world applications, such as human-computer interaction, surveillance, video retrieval, retirement home monitoring, and robotics. In the past decade, it has attracted a great amount of interest in the research community. Recently, the commoditization of depth sensors has generated much excitement in action recognition from depth sensors. New depth sensor technology has enabled many applications that were not feasible before. On one hand, action recognition becomes far easier with depth sensors. On the other hand, the drive to recognize more complex actions presents new challenges. One crucial aspect of action recognition is to extract discriminative features. The depth maps have completely different characteristics from the RGB images. Directly applying features designed for RGB images does not work. Complex actions usually involve complicated temporal structures, human-object interactions, and person-person contacts. New machine learning algorithms need to be developed to learn these complex structures. This work enables the reader to quickly familiarize themselves with the latest research in depth-sensor based action recognition, and to gain a deeper understanding of recently developed techniques. It will be of great use for both researchers and practitioners who are interested in human action recognition with depth sensors. The text focuses on feature representation and machine learning algorithms for action recognition from depth sensors. After presenting a comprehensive overview of the state of the art in action recognition from depth data, the authors then provide in-depth descriptions of their recently developed feature representations and machine learning techniques, including lower-level depth and skeleton features, higher-level representations to model the temporal structure and human-object interactions, and feature selection techniques for occlusion handling. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Computer vision. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Biometrics. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Image Processing and Computer Vision. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Biometrics. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
User Interfaces and Human Computer Interaction. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Liu, Zicheng. |
| Relator term |
author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Wu, Ying. |
| Relator term |
author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY |
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| Title |
Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Display text |
Printed edition: |
| International Standard Book Number |
9783319045603 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
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| Uniform title |
SpringerBriefs in Computer Science, |
| -- |
2191-5768 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="http://dx.doi.org/10.1007/978-3-319-04561-0">http://dx.doi.org/10.1007/978-3-319-04561-0</a> |
| 912 ## - |
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ZDB-2-SCS |
