Yubo Yuan

Prof. Dr. Yubo Yuan

orcid

Research Focus & Metrics

Academic Career Length

22Years (2005-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2011 in Neurocomputing
Verified
Journal Article
Closed access
YWYuguang Wang
Yuguang Wang
FCFeilong Cao
Feilong Cao
YYYubo Yuan
Yubo Yuan

25 June 2014 in The Scientific World Journal
Verified
Journal Article
Open access
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ZCZhi-Hua Chen
Zhi-Hua Chen
JKJung-Tae Kim
Jung-Tae Kim
JLJianning Liang
Jianning Liang
JZJing Zhang
Jing Zhang
YYYubo Yuan
Yubo Yuan
Hand gesture recognition is very significant for human-computer interaction. In this work, we present a novel real-time method for hand gesture recognition. In our framework, the hand region is extracted from the background with the background subtraction method. Then, the palm and fingers are segmented so as to detect and recognize the fingers. Finally, a rule classifier is applied to predict the labels of hand gestures. The experiments on the data set of 1300 images show that our method performs well and is highly efficient. Moreover, our method shows better performance than a state-of-art method on another data set of hand gestures.

1 September 2011 in Neurocomputing
Verified
Journal Article
Closed access
YYYubo Yuan
Yubo Yuan
YWYuguang Wang
Yuguang Wang
FCFeilong Cao
Feilong Cao

1 January 2007 in Journal of Industrial and Management Optimization
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Journal Article
Open access
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YYYubo Yuan
Yubo Yuan
WFWeiguo Fan
Weiguo Fan
DPDongmei Pu
Dongmei Pu

1 February 2013 in Mathematical and Computer Modelling
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Journal Article
Closed access
YYYubo Yuan
Yubo Yuan

29 April 2014 in The Scientific World Journal
Verified
Journal Article
Open access
PDF file
MWMing Wu
Ming Wu
YYYubo Yuan
Yubo Yuan
This paper presents a novel gender classification method based on geometry features of palm image which is simple, fast, and easy to handle. This gender classification method based on geometry features comprises two main attributes. The first one is feature extraction by image processing. The other one is classification system with polynomial smooth support vector machine (PSSVM). A total of 180 palm images were collected from 30 persons to verify the validity of the proposed gender classification approach and the results are satisfactory with classification rate over 85%. Experimental results demonstrate that our proposed approach is feasible and effective in gender recognition.