Hand Gesture Recognition Using Fast Multi-scale Analysis
Hand gesture has been used as a natural and efficient way in human computer interaction. Due to independence of auxiliary input devices, vision-based hand interfaces is more favorable for users. However, the process of hand gesture recognition is very time consuming, which often brings much frustrat...
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| creator | Yikai Fang Jian Cheng Kongqiao Wang Hanqing Lu |
| description | Hand gesture has been used as a natural and efficient way in human computer interaction. Due to independence of auxiliary input devices, vision-based hand interfaces is more favorable for users. However, the process of hand gesture recognition is very time consuming, which often brings much frustration to users. In this paper, we propose a fast feature detection and description approach which can significantly speed up hand gesture recognition. Firstly, integral image is used to approximate Gaussian derivatives to calculate image convolution in feature detection. Then multi-scale geometric descriptors at feature points are obtained to represent hand gestures. Finally gesture is recognized with its geometric configuration. Experiments show that the proposed method needs much less time consumption while obtains comparative performance with its counterpart in literatures. |
| doi_str_mv | 10.1109/ICIG.2007.52 |
| format | Conference Proceeding |
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Due to independence of auxiliary input devices, vision-based hand interfaces is more favorable for users. However, the process of hand gesture recognition is very time consuming, which often brings much frustration to users. In this paper, we propose a fast feature detection and description approach which can significantly speed up hand gesture recognition. Firstly, integral image is used to approximate Gaussian derivatives to calculate image convolution in feature detection. Then multi-scale geometric descriptors at feature points are obtained to represent hand gestures. Finally gesture is recognized with its geometric configuration. Experiments show that the proposed method needs much less time consumption while obtains comparative performance with its counterpart in literatures.</description><identifier>ISBN: 9780769529295</identifier><identifier>ISBN: 0769529291</identifier><identifier>DOI: 10.1109/ICIG.2007.52</identifier><language>eng</language><publisher>IEEE</publisher><subject>Boosting ; Computational efficiency ; Computer vision ; Convolution ; Detectors ; Graphics ; Human computer interaction ; Pattern recognition ; Pervasive computing ; Pixel</subject><ispartof>Fourth International Conference on Image and Graphics (ICIG 2007), 2007, p.694-698</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/4297171$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4297171$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yikai Fang</creatorcontrib><creatorcontrib>Jian Cheng</creatorcontrib><creatorcontrib>Kongqiao Wang</creatorcontrib><creatorcontrib>Hanqing Lu</creatorcontrib><title>Hand Gesture Recognition Using Fast Multi-scale Analysis</title><title>Fourth International Conference on Image and Graphics (ICIG 2007)</title><addtitle>ICIG</addtitle><description>Hand gesture has been used as a natural and efficient way in human computer interaction. Due to independence of auxiliary input devices, vision-based hand interfaces is more favorable for users. However, the process of hand gesture recognition is very time consuming, which often brings much frustration to users. In this paper, we propose a fast feature detection and description approach which can significantly speed up hand gesture recognition. Firstly, integral image is used to approximate Gaussian derivatives to calculate image convolution in feature detection. Then multi-scale geometric descriptors at feature points are obtained to represent hand gestures. Finally gesture is recognized with its geometric configuration. Experiments show that the proposed method needs much less time consumption while obtains comparative performance with its counterpart in literatures.</description><subject>Boosting</subject><subject>Computational efficiency</subject><subject>Computer vision</subject><subject>Convolution</subject><subject>Detectors</subject><subject>Graphics</subject><subject>Human computer interaction</subject><subject>Pattern recognition</subject><subject>Pervasive computing</subject><subject>Pixel</subject><isbn>9780769529295</isbn><isbn>0769529291</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjUFLwzAYQAMiKLM3b17yB1rz5Uua5DiK6wobgrjzSNsvI1I7abLD_r0DfZd3e4-xZxAVgHCvXdO1lRTCVFrescIZK0zttHTS6QdWpPQlbqBTRtpHZrd-HnlLKV8W4h80nE9zzPE880OK84lvfMp8f5lyLNPgJ-Lr2U_XFNMTuw9-SlT8e8UOm7fPZlvu3tuuWe_KCEbnEvF2AgrQ98LKHkciqFFh31sr6-AGPYIcQ1CDJl-jRQv1CDgINAZUCLhiL3_dSETHnyV---V6VNIZMIC_k61Dqg</recordid><startdate>200708</startdate><enddate>200708</enddate><creator>Yikai Fang</creator><creator>Jian Cheng</creator><creator>Kongqiao Wang</creator><creator>Hanqing Lu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200708</creationdate><title>Hand Gesture Recognition Using Fast Multi-scale Analysis</title><author>Yikai Fang ; Jian Cheng ; Kongqiao Wang ; Hanqing Lu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-333941ef1bb082b3dee16343bb8826f9c5d12dff4c5ea6383816d13c037714ff3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Boosting</topic><topic>Computational efficiency</topic><topic>Computer vision</topic><topic>Convolution</topic><topic>Detectors</topic><topic>Graphics</topic><topic>Human computer interaction</topic><topic>Pattern recognition</topic><topic>Pervasive computing</topic><topic>Pixel</topic><toplevel>online_resources</toplevel><creatorcontrib>Yikai Fang</creatorcontrib><creatorcontrib>Jian Cheng</creatorcontrib><creatorcontrib>Kongqiao Wang</creatorcontrib><creatorcontrib>Hanqing Lu</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>Yikai Fang</au><au>Jian Cheng</au><au>Kongqiao Wang</au><au>Hanqing Lu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Hand Gesture Recognition Using Fast Multi-scale Analysis</atitle><btitle>Fourth International Conference on Image and Graphics (ICIG 2007)</btitle><stitle>ICIG</stitle><date>2007-08</date><risdate>2007</risdate><spage>694</spage><epage>698</epage><pages>694-698</pages><isbn>9780769529295</isbn><isbn>0769529291</isbn><abstract>Hand gesture has been used as a natural and efficient way in human computer interaction. Due to independence of auxiliary input devices, vision-based hand interfaces is more favorable for users. However, the process of hand gesture recognition is very time consuming, which often brings much frustration to users. In this paper, we propose a fast feature detection and description approach which can significantly speed up hand gesture recognition. Firstly, integral image is used to approximate Gaussian derivatives to calculate image convolution in feature detection. Then multi-scale geometric descriptors at feature points are obtained to represent hand gestures. Finally gesture is recognized with its geometric configuration. Experiments show that the proposed method needs much less time consumption while obtains comparative performance with its counterpart in literatures.</abstract><pub>IEEE</pub><doi>10.1109/ICIG.2007.52</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Boosting Computational efficiency Computer vision Convolution Detectors Graphics Human computer interaction Pattern recognition Pervasive computing Pixel |
| title | Hand Gesture Recognition Using Fast Multi-scale Analysis |
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