Real-time hand posture analysis based on neural network

In this paper, a modified Neural Gas algorithm is proposed and used to approximate hand topology. As original Neural Gas algorithm is intractable for real-time applications, some optimization such as unnecessary adaption removal and simple learning rate function are introduced to make it applicable...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Yang Shi, Xiang Chen, Kongqiao Wang, Yikai Fang, Lei Xu
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 896
container_issue
container_start_page 893
container_title
container_volume
creator Yang Shi
Xiang Chen
Kongqiao Wang
Yikai Fang
Lei Xu
description In this paper, a modified Neural Gas algorithm is proposed and used to approximate hand topology. As original Neural Gas algorithm is intractable for real-time applications, some optimization such as unnecessary adaption removal and simple learning rate function are introduced to make it applicable for real-time applications. With segmented hand area, the topology representation can be obtained based on neural network. The topology based representation of hand shape will further facilitate both fingertip localization and posture recognition. Experiments show the accuracy and the speed of our method can satisfy realtime requirements of interaction applications, even on mobile devices.
doi_str_mv 10.1109/ICOSP.2010.5656041
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5656041</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5656041</ieee_id><sourcerecordid>5656041</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-22fbbb8c8b0ca9e07d18d173d1b907ef148f378b73783c985f9c5d0522e38c783</originalsourceid><addsrcrecordid>eNpVT81KAzEYjKhgqfsCeskLbM2XTTbJURZ_CoVKq-eSny-4ut0tmy3StzdgL85hhpnDMEPIHbAFADMPy2a9fVtwlr2sZc0EXJDCKA2CCyG1Mfryn1fiisw41KKUnMMNKVL6YhmSK27qGVEbtF05tXukn7YP9DCk6Tgitb3tTqlN1NmEgQ497fE42i7L9DOM37fkOtouYXHWOfl4fnpvXsvV-mXZPK7KFpScSs6jc0577Zi3BpkKoAOoKoAzTGEEoWOltFOZKm-0jMbLkNdxrLTP2Zzc__W2iLg7jO3ejqfd-Xr1C9u8Scg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Real-time hand posture analysis based on neural network</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Yang Shi ; Xiang Chen ; Kongqiao Wang ; Yikai Fang ; Lei Xu</creator><creatorcontrib>Yang Shi ; Xiang Chen ; Kongqiao Wang ; Yikai Fang ; Lei Xu</creatorcontrib><description>In this paper, a modified Neural Gas algorithm is proposed and used to approximate hand topology. As original Neural Gas algorithm is intractable for real-time applications, some optimization such as unnecessary adaption removal and simple learning rate function are introduced to make it applicable for real-time applications. With segmented hand area, the topology representation can be obtained based on neural network. The topology based representation of hand shape will further facilitate both fingertip localization and posture recognition. Experiments show the accuracy and the speed of our method can satisfy realtime requirements of interaction applications, even on mobile devices.</description><identifier>ISSN: 2164-5221</identifier><identifier>ISBN: 9781424458974</identifier><identifier>ISBN: 1424458978</identifier><identifier>EISBN: 9781424458998</identifier><identifier>EISBN: 9781424459001</identifier><identifier>EISBN: 1424459001</identifier><identifier>EISBN: 1424458994</identifier><identifier>DOI: 10.1109/ICOSP.2010.5656041</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; camera-projector system ; Gesture recognition ; hand posture recognition ; Network topology ; Neural Gas ; Real time systems ; Shape ; shape represention ; Topology ; Training</subject><ispartof>IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010, p.893-896</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5656041$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5656041$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yang Shi</creatorcontrib><creatorcontrib>Xiang Chen</creatorcontrib><creatorcontrib>Kongqiao Wang</creatorcontrib><creatorcontrib>Yikai Fang</creatorcontrib><creatorcontrib>Lei Xu</creatorcontrib><title>Real-time hand posture analysis based on neural network</title><title>IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS</title><addtitle>ICOSP</addtitle><description>In this paper, a modified Neural Gas algorithm is proposed and used to approximate hand topology. As original Neural Gas algorithm is intractable for real-time applications, some optimization such as unnecessary adaption removal and simple learning rate function are introduced to make it applicable for real-time applications. With segmented hand area, the topology representation can be obtained based on neural network. The topology based representation of hand shape will further facilitate both fingertip localization and posture recognition. Experiments show the accuracy and the speed of our method can satisfy realtime requirements of interaction applications, even on mobile devices.</description><subject>Artificial neural networks</subject><subject>camera-projector system</subject><subject>Gesture recognition</subject><subject>hand posture recognition</subject><subject>Network topology</subject><subject>Neural Gas</subject><subject>Real time systems</subject><subject>Shape</subject><subject>shape represention</subject><subject>Topology</subject><subject>Training</subject><issn>2164-5221</issn><isbn>9781424458974</isbn><isbn>1424458978</isbn><isbn>9781424458998</isbn><isbn>9781424459001</isbn><isbn>1424459001</isbn><isbn>1424458994</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVT81KAzEYjKhgqfsCeskLbM2XTTbJURZ_CoVKq-eSny-4ut0tmy3StzdgL85hhpnDMEPIHbAFADMPy2a9fVtwlr2sZc0EXJDCKA2CCyG1Mfryn1fiisw41KKUnMMNKVL6YhmSK27qGVEbtF05tXukn7YP9DCk6Tgitb3tTqlN1NmEgQ497fE42i7L9DOM37fkOtouYXHWOfl4fnpvXsvV-mXZPK7KFpScSs6jc0577Zi3BpkKoAOoKoAzTGEEoWOltFOZKm-0jMbLkNdxrLTP2Zzc__W2iLg7jO3ejqfd-Xr1C9u8Scg</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Yang Shi</creator><creator>Xiang Chen</creator><creator>Kongqiao Wang</creator><creator>Yikai Fang</creator><creator>Lei Xu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201010</creationdate><title>Real-time hand posture analysis based on neural network</title><author>Yang Shi ; Xiang Chen ; Kongqiao Wang ; Yikai Fang ; Lei Xu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-22fbbb8c8b0ca9e07d18d173d1b907ef148f378b73783c985f9c5d0522e38c783</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Artificial neural networks</topic><topic>camera-projector system</topic><topic>Gesture recognition</topic><topic>hand posture recognition</topic><topic>Network topology</topic><topic>Neural Gas</topic><topic>Real time systems</topic><topic>Shape</topic><topic>shape represention</topic><topic>Topology</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Yang Shi</creatorcontrib><creatorcontrib>Xiang Chen</creatorcontrib><creatorcontrib>Kongqiao Wang</creatorcontrib><creatorcontrib>Yikai Fang</creatorcontrib><creatorcontrib>Lei Xu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yang Shi</au><au>Xiang Chen</au><au>Kongqiao Wang</au><au>Yikai Fang</au><au>Lei Xu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Real-time hand posture analysis based on neural network</atitle><btitle>IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS</btitle><stitle>ICOSP</stitle><date>2010-10</date><risdate>2010</risdate><spage>893</spage><epage>896</epage><pages>893-896</pages><issn>2164-5221</issn><isbn>9781424458974</isbn><isbn>1424458978</isbn><eisbn>9781424458998</eisbn><eisbn>9781424459001</eisbn><eisbn>1424459001</eisbn><eisbn>1424458994</eisbn><abstract>In this paper, a modified Neural Gas algorithm is proposed and used to approximate hand topology. As original Neural Gas algorithm is intractable for real-time applications, some optimization such as unnecessary adaption removal and simple learning rate function are introduced to make it applicable for real-time applications. With segmented hand area, the topology representation can be obtained based on neural network. The topology based representation of hand shape will further facilitate both fingertip localization and posture recognition. Experiments show the accuracy and the speed of our method can satisfy realtime requirements of interaction applications, even on mobile devices.</abstract><pub>IEEE</pub><doi>10.1109/ICOSP.2010.5656041</doi><tpages>4</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2164-5221
ispartof IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010, p.893-896
issn 2164-5221
language eng
recordid cdi_ieee_primary_5656041
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Artificial neural networks
camera-projector system
Gesture recognition
hand posture recognition
Network topology
Neural Gas
Real time systems
Shape
shape represention
Topology
Training
title Real-time hand posture analysis based on neural network
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T06%3A33%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Real-time%20hand%20posture%20analysis%20based%20on%20neural%20network&rft.btitle=IEEE%2010th%20INTERNATIONAL%20CONFERENCE%20ON%20SIGNAL%20PROCESSING%20PROCEEDINGS&rft.au=Yang%20Shi&rft.date=2010-10&rft.spage=893&rft.epage=896&rft.pages=893-896&rft.issn=2164-5221&rft.isbn=9781424458974&rft.isbn_list=1424458978&rft_id=info:doi/10.1109/ICOSP.2010.5656041&rft_dat=%3Cieee_6IE%3E5656041%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424458998&rft.eisbn_list=9781424459001&rft.eisbn_list=1424459001&rft.eisbn_list=1424458994&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5656041&rfr_iscdi=true