Presentation 2010-03-16
Hand Shape Recognition based on SVM and online learning with HOG
Ryousuke MUTOU, Kazutaka SIMADA, Tutomu ENDOU,
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Abstract(in English) In this paper, we proposed a combined method for hand shape recognition. It consists of support vector machines (SVMs) and an online learning algorithm based on the percepron. We apply HOG features to each method. First, our method estimates a hand shape of an input image by using SVMs. Also the online learning method with the perceptron uses the input image as training data if the data possesses a high confidence score in the recognition process. Next, we select the final hand shape from the outputs from the SVMs and perceptron by using the score from SVMs. We compared the combined method with SVMs. The experimental results show the effectiveness of the proposed method.
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Keyword(in English) hand shape recognition / online learning / HOG
Paper # PRMU2009-311,HIP2009-196
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Conference Information
Committee PRMU
Conference Date 2010/3/8(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Hand Shape Recognition based on SVM and online learning with HOG
Sub Title (in English)
Keyword(1) hand shape recognition
Keyword(2) online learning
Keyword(3) HOG
1st Author's Name Ryousuke MUTOU
1st Author's Affiliation Kyushu Institute of Technology Computer Science and Systems Engineering()
2nd Author's Name Kazutaka SIMADA
2nd Author's Affiliation Kyushu Institute of Technology Computer Science and Systems Engineering
3rd Author's Name Tutomu ENDOU
3rd Author's Affiliation Kyushu Institute of Technology Computer Science and Systems Engineering
Date 2010-03-16
Paper # PRMU2009-311,HIP2009-196
Volume (vol) vol.109
Number (no) 470
Page pp.pp.-
#Pages 6
Date of Issue