Multimedia content analysis on gesture event detection for a SMART TV Keyboard application
Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shap...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Togootogtokh, Enkhtogtokh [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media New York 2016 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 76(2016), 5 vom: 05. März, Seite 7341-7363 |
|---|---|
| Übergeordnetes Werk: |
volume:76 ; year:2016 ; number:5 ; day:05 ; month:03 ; pages:7341-7363 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-016-3385-3 |
|---|
| Katalog-ID: |
OLC2035032113 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2035032113 | ||
| 003 | DE-627 | ||
| 005 | 20230503193159.0 | ||
| 007 | tu | ||
| 008 | 200819s2016 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11042-016-3385-3 |2 doi | |
| 035 | |a (DE-627)OLC2035032113 | ||
| 035 | |a (DE-He213)s11042-016-3385-3-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 070 |a 004 |q VZ |
| 100 | 1 | |a Togootogtokh, Enkhtogtokh |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Multimedia content analysis on gesture event detection for a SMART TV Keyboard application |
| 264 | 1 | |c 2016 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media New York 2016 | ||
| 520 | |a Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/. | ||
| 650 | 4 | |a Gesture event detection | |
| 650 | 4 | |a Gesture event recognition | |
| 650 | 4 | |a Computer vision | |
| 650 | 4 | |a Machine learning for gesture event detection | |
| 650 | 4 | |a SMART TV Keyboard | |
| 700 | 1 | |a Shih, Timothy K. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Springer US, 1995 |g 76(2016), 5 vom: 05. März, Seite 7341-7363 |w (DE-627)189064145 |w (DE-600)1287642-2 |w (DE-576)052842126 |x 1380-7501 |7 nnns |
| 773 | 1 | 8 | |g volume:76 |g year:2016 |g number:5 |g day:05 |g month:03 |g pages:7341-7363 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11042-016-3385-3 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-BUB | ||
| 912 | |a SSG-OLC-MKW | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 76 |j 2016 |e 5 |b 05 |c 03 |h 7341-7363 | ||
| author_variant |
e t et t k s tk tks |
|---|---|
| matchkey_str |
article:13807501:2016----::utmdaotnaayiogsuevndtcinoamr |
| hierarchy_sort_str |
2016 |
| publishDate |
2016 |
| allfields |
10.1007/s11042-016-3385-3 doi (DE-627)OLC2035032113 (DE-He213)s11042-016-3385-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Togootogtokh, Enkhtogtokh verfasserin aut Multimedia content analysis on gesture event detection for a SMART TV Keyboard application 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/. Gesture event detection Gesture event recognition Computer vision Machine learning for gesture event detection SMART TV Keyboard Shih, Timothy K. aut Enthalten in Multimedia tools and applications Springer US, 1995 76(2016), 5 vom: 05. März, Seite 7341-7363 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:76 year:2016 number:5 day:05 month:03 pages:7341-7363 https://doi.org/10.1007/s11042-016-3385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 76 2016 5 05 03 7341-7363 |
| spelling |
10.1007/s11042-016-3385-3 doi (DE-627)OLC2035032113 (DE-He213)s11042-016-3385-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Togootogtokh, Enkhtogtokh verfasserin aut Multimedia content analysis on gesture event detection for a SMART TV Keyboard application 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/. Gesture event detection Gesture event recognition Computer vision Machine learning for gesture event detection SMART TV Keyboard Shih, Timothy K. aut Enthalten in Multimedia tools and applications Springer US, 1995 76(2016), 5 vom: 05. März, Seite 7341-7363 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:76 year:2016 number:5 day:05 month:03 pages:7341-7363 https://doi.org/10.1007/s11042-016-3385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 76 2016 5 05 03 7341-7363 |
| allfields_unstemmed |
10.1007/s11042-016-3385-3 doi (DE-627)OLC2035032113 (DE-He213)s11042-016-3385-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Togootogtokh, Enkhtogtokh verfasserin aut Multimedia content analysis on gesture event detection for a SMART TV Keyboard application 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/. Gesture event detection Gesture event recognition Computer vision Machine learning for gesture event detection SMART TV Keyboard Shih, Timothy K. aut Enthalten in Multimedia tools and applications Springer US, 1995 76(2016), 5 vom: 05. März, Seite 7341-7363 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:76 year:2016 number:5 day:05 month:03 pages:7341-7363 https://doi.org/10.1007/s11042-016-3385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 76 2016 5 05 03 7341-7363 |
| allfieldsGer |
10.1007/s11042-016-3385-3 doi (DE-627)OLC2035032113 (DE-He213)s11042-016-3385-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Togootogtokh, Enkhtogtokh verfasserin aut Multimedia content analysis on gesture event detection for a SMART TV Keyboard application 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/. Gesture event detection Gesture event recognition Computer vision Machine learning for gesture event detection SMART TV Keyboard Shih, Timothy K. aut Enthalten in Multimedia tools and applications Springer US, 1995 76(2016), 5 vom: 05. März, Seite 7341-7363 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:76 year:2016 number:5 day:05 month:03 pages:7341-7363 https://doi.org/10.1007/s11042-016-3385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 76 2016 5 05 03 7341-7363 |
| allfieldsSound |
10.1007/s11042-016-3385-3 doi (DE-627)OLC2035032113 (DE-He213)s11042-016-3385-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Togootogtokh, Enkhtogtokh verfasserin aut Multimedia content analysis on gesture event detection for a SMART TV Keyboard application 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/. Gesture event detection Gesture event recognition Computer vision Machine learning for gesture event detection SMART TV Keyboard Shih, Timothy K. aut Enthalten in Multimedia tools and applications Springer US, 1995 76(2016), 5 vom: 05. März, Seite 7341-7363 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:76 year:2016 number:5 day:05 month:03 pages:7341-7363 https://doi.org/10.1007/s11042-016-3385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 76 2016 5 05 03 7341-7363 |
| language |
English |
| source |
Enthalten in Multimedia tools and applications 76(2016), 5 vom: 05. März, Seite 7341-7363 volume:76 year:2016 number:5 day:05 month:03 pages:7341-7363 |
| sourceStr |
Enthalten in Multimedia tools and applications 76(2016), 5 vom: 05. März, Seite 7341-7363 volume:76 year:2016 number:5 day:05 month:03 pages:7341-7363 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Gesture event detection Gesture event recognition Computer vision Machine learning for gesture event detection SMART TV Keyboard |
| dewey-raw |
070 |
| isfreeaccess_bool |
false |
| container_title |
Multimedia tools and applications |
| authorswithroles_txt_mv |
Togootogtokh, Enkhtogtokh @@aut@@ Shih, Timothy K. @@aut@@ |
| publishDateDaySort_date |
2016-03-05T00:00:00Z |
| hierarchy_top_id |
189064145 |
| dewey-sort |
270 |
| id |
OLC2035032113 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2035032113</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503193159.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-016-3385-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2035032113</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-016-3385-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Togootogtokh, Enkhtogtokh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multimedia content analysis on gesture event detection for a SMART TV Keyboard application</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gesture event detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gesture event recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer vision</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning for gesture event detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SMART TV Keyboard</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shih, Timothy K.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">76(2016), 5 vom: 05. März, Seite 7341-7363</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:76</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:5</subfield><subfield code="g">day:05</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:7341-7363</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-016-3385-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">76</subfield><subfield code="j">2016</subfield><subfield code="e">5</subfield><subfield code="b">05</subfield><subfield code="c">03</subfield><subfield code="h">7341-7363</subfield></datafield></record></collection>
|
| author |
Togootogtokh, Enkhtogtokh |
| spellingShingle |
Togootogtokh, Enkhtogtokh ddc 070 misc Gesture event detection misc Gesture event recognition misc Computer vision misc Machine learning for gesture event detection misc SMART TV Keyboard Multimedia content analysis on gesture event detection for a SMART TV Keyboard application |
| authorStr |
Togootogtokh, Enkhtogtokh |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)189064145 |
| format |
Article |
| dewey-ones |
070 - News media, journalism & publishing 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1380-7501 |
| topic_title |
070 004 VZ Multimedia content analysis on gesture event detection for a SMART TV Keyboard application Gesture event detection Gesture event recognition Computer vision Machine learning for gesture event detection SMART TV Keyboard |
| topic |
ddc 070 misc Gesture event detection misc Gesture event recognition misc Computer vision misc Machine learning for gesture event detection misc SMART TV Keyboard |
| topic_unstemmed |
ddc 070 misc Gesture event detection misc Gesture event recognition misc Computer vision misc Machine learning for gesture event detection misc SMART TV Keyboard |
| topic_browse |
ddc 070 misc Gesture event detection misc Gesture event recognition misc Computer vision misc Machine learning for gesture event detection misc SMART TV Keyboard |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Multimedia tools and applications |
| hierarchy_parent_id |
189064145 |
| dewey-tens |
070 - News media, journalism & publishing 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Multimedia tools and applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 |
| title |
Multimedia content analysis on gesture event detection for a SMART TV Keyboard application |
| ctrlnum |
(DE-627)OLC2035032113 (DE-He213)s11042-016-3385-3-p |
| title_full |
Multimedia content analysis on gesture event detection for a SMART TV Keyboard application |
| author_sort |
Togootogtokh, Enkhtogtokh |
| journal |
Multimedia tools and applications |
| journalStr |
Multimedia tools and applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
txt |
| container_start_page |
7341 |
| author_browse |
Togootogtokh, Enkhtogtokh Shih, Timothy K. |
| container_volume |
76 |
| class |
070 004 VZ |
| format_se |
Aufsätze |
| author-letter |
Togootogtokh, Enkhtogtokh |
| doi_str_mv |
10.1007/s11042-016-3385-3 |
| dewey-full |
070 004 |
| title_sort |
multimedia content analysis on gesture event detection for a smart tv keyboard application |
| title_auth |
Multimedia content analysis on gesture event detection for a SMART TV Keyboard application |
| abstract |
Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/. © Springer Science+Business Media New York 2016 |
| abstractGer |
Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/. © Springer Science+Business Media New York 2016 |
| abstract_unstemmed |
Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/. © Springer Science+Business Media New York 2016 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 |
| container_issue |
5 |
| title_short |
Multimedia content analysis on gesture event detection for a SMART TV Keyboard application |
| url |
https://doi.org/10.1007/s11042-016-3385-3 |
| remote_bool |
false |
| author2 |
Shih, Timothy K. |
| author2Str |
Shih, Timothy K. |
| ppnlink |
189064145 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11042-016-3385-3 |
| up_date |
2024-07-03T23:31:12.295Z |
| _version_ |
1803602595468541952 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2035032113</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503193159.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-016-3385-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2035032113</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-016-3385-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Togootogtokh, Enkhtogtokh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multimedia content analysis on gesture event detection for a SMART TV Keyboard application</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method is based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. In this paper, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries. More information and demonstrations of the proposed keyboard can be accessed at http://video.minelab.tw/MCAoGED/.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gesture event detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gesture event recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer vision</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning for gesture event detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SMART TV Keyboard</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shih, Timothy K.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">76(2016), 5 vom: 05. März, Seite 7341-7363</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:76</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:5</subfield><subfield code="g">day:05</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:7341-7363</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-016-3385-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">76</subfield><subfield code="j">2016</subfield><subfield code="e">5</subfield><subfield code="b">05</subfield><subfield code="c">03</subfield><subfield code="h">7341-7363</subfield></datafield></record></collection>
|
| score |
7.399585 |