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| Research on Hand Gesture Recognition Based on Inner-distance Shape Context and Bag of Words Model |
| Qiuyu Zhang, Huiyi Wei, Zhigang Xu, Moyi Zhang, Hongxiang Duan, Lu Lv |
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Abstract A method of gesture recognition based on inner-distance shape
context and bag of words (Inner-distance Shape Context - Bag of
Words, IDSC-BOW) is proposed in this paper, mainly aimed at the
influence on the accuracy of recognition in representing process of
gestures caused by joints or part structures deformations. Firstly,
the method uses elliptical skin model to segment, get the binary
gesture area and extract contours. Then, sample points on the
contour are obtained by uniform sampling. The visual dictionary is
generated through K-means clustering with inner-distance shape
context features of hands. The generated visual dictionary is used
to map the inner-distance shape context features of gestures into a
collection of visual words. The BOW vectors are obtained by process
of frequency statistic and normalization on the visual words.
Finally, the Support Vector Machine (SVM) classifier is used for
classification. The experimental results show that the method has
higher recognition rate on ten kinds of hand gestures as $0-9$.
It keeps good robustness on joints of hands and part structures
deformations.
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| DOI:10.12733/jics20103703 |
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