Presentation 2012/1/12
Local Feature Transform with RCA and Application to general object recognition for life support
Tomoki NISHIMURA, Haiyuan WU, Hirokazu TAKI, Hirokazu MIURA,
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Abstract(in English) In this paper, we propose a method for transforming local feature using relevant component analysis (RCA). Our method analyzes the distribution of E-SIFT local features provided from a training image set, and makes the difference of visual word clear by adjusting distance between local features that followed the density of the distribution. In this paper, we do mind an application to life supporting system. This method's effectiveness was confirmed through several comparison experiments using common database of everyday life article.
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Keyword(in English) Generic object recognition / detection of feature vector / SIFT feature vector / relevant component analysis
Paper # MVE2011-63,PRMU2011-154
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Committee MVE
Conference Date 2012/1/12(1days)
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Registration To Media Experience and Virtual Environment (MVE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Local Feature Transform with RCA and Application to general object recognition for life support
Sub Title (in English)
Keyword(1) Generic object recognition
Keyword(2) detection of feature vector
Keyword(3) SIFT feature vector
Keyword(4) relevant component analysis
1st Author's Name Tomoki NISHIMURA
1st Author's Affiliation Wakayama University system department of engineering()
2nd Author's Name Haiyuan WU
2nd Author's Affiliation Wakayama University system department of engineering
3rd Author's Name Hirokazu TAKI
3rd Author's Affiliation Wakayama University system department of engineering
4th Author's Name Hirokazu MIURA
4th Author's Affiliation Wakayama University system department of engineering
Date 2012/1/12
Paper # MVE2011-63,PRMU2011-154
Volume (vol) vol.111
Number (no) 380
Page pp.pp.-
#Pages 6
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