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Chin-Chen Chang, Jiann-Jone Chen1, Wen-Kai Tai2 and Chin-Chuan Han
Department of Computer Science and Information Engineering
National United University
Miaoli, 360 Taiwan
1Department of Electrical Engineering
National Taiwan University of Science and Technology
Taipei, 106 Taiwan
2Department of Computer Science and Information Engineering
National Dong Hwa University
Hualien, 974 Taiwan
Static gestures can convey certain meanings and act as specific transitions in dynamic
gestures. Therefore, recognizing static gestures is one of the most important aspects
of gesture recognition. In this paper, a new approach is presented for recognizing
static gestures based on Zernike moments (ZMs) and pseudo-Zernike moments (PZMs).
The binary hand silhouette is first accommodated with a minimum bounding circle
(MBC). The binary hand silhouette is then decomposed into the finger part and the palm
part by morphological operations according to the radius of the MBC. After that, the
ZMs & PZMs of the finger part and the palm part with different importance, respectively,
are computed based on the center of the MBC. Finally, 1-nearest neighbor techniques are
used to perform feature matching between an input feature vector and stored feature
vectors for static gesture identification. Results show that the proposed approach performs
better than previous methods based on conventional ZMs & PZMs in recognizing
static gestures. The proposed technique could be useful in improving the recognition rate
of static gestures.
Received August 16, 2005; accepted January 17, 2006.
Communicated by Jhing-Fa Wang, Pau-Choo Chung and Mark Billinghurst.
*A preliminary version of this paper has been presented at International Workshop on Advanced Image Technology
2002 (IWAIT 2002), Hualien, Taiwan, R.O.C., 2002.