@article{(Open Science Index):https://publications.waset.org/pdf/1761,
	  title     = {Hand Gesture Recognition Based on Combined Features Extraction},
	  author    = {Mahmoud Elmezain and  Ayoub Al-Hamadi and  Bernd Michaelis},
	  country	= {},
	  institution	= {},
	  abstract     = {Hand gesture is an active area of research in the vision
community, mainly for the purpose of sign language recognition and
Human Computer Interaction. In this paper, we propose a system to
recognize alphabet characters (A-Z) and numbers (0-9) in real-time
from stereo color image sequences using Hidden Markov Models
(HMMs). Our system is based on three main stages; automatic segmentation
and preprocessing of the hand regions, feature extraction
and classification. In automatic segmentation and preprocessing stage,
color and 3D depth map are used to detect hands where the hand
trajectory will take place in further step using Mean-shift algorithm
and Kalman filter. In the feature extraction stage, 3D combined features
of location, orientation and velocity with respected to Cartesian
systems are used. And then, k-means clustering is employed for
HMMs codeword. The final stage so-called classification, Baum-
Welch algorithm is used to do a full train for HMMs parameters.
The gesture of alphabets and numbers is recognized using Left-Right
Banded model in conjunction with Viterbi algorithm. Experimental
results demonstrate that, our system can successfully recognize hand
gestures with 98.33% recognition rate.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {3},
	  number    = {12},
	  year      = {2009},
	  pages     = {2389 - 2394},
	  ee        = {https://publications.waset.org/pdf/1761},
	  url   	= {https://publications.waset.org/vol/36},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 36, 2009},