Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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In this paper we propose a system for dynamic hand gesture recognition of Arabic Sign Language. The proposed system takes the dynamic gesture (video stream) as input, extracts hand area and computes hand motion features, then uses these features to recognize the gesture. The system identifies the hand blob using YCbCr color space to detect skin color of hand. The system classifies the input pattern based on correlation coefficients matching technique. The significance of the system is its simplicity and ability to recognize the gestures independent of skin color and physical structure of the performers. The experiment results show that the gesture recognition rate of 20 different signs, performed by 8 different signers, is 85.67%.
Mohamed sameer Mohamed Abdalla. 2013. \u201cDynamic Hand Gesture Recognition of Arabic Sign Language using Hand Motion Trajectory Features\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 13 (GJCST Volume 13 Issue F5): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 102
Country: Egypt
Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision
Authors: Mohamed S. Abdalla, Elsayed E. Hemayed (PhD/Dr. count: 0)
View Count (all-time): 251
Total Views (Real + Logic): 9394
Total Downloads (simulated): 2461
Publish Date: 2013 06, Sat
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In this paper we propose a system for dynamic hand gesture recognition of Arabic Sign Language. The proposed system takes the dynamic gesture (video stream) as input, extracts hand area and computes hand motion features, then uses these features to recognize the gesture. The system identifies the hand blob using YCbCr color space to detect skin color of hand. The system classifies the input pattern based on correlation coefficients matching technique. The significance of the system is its simplicity and ability to recognize the gestures independent of skin color and physical structure of the performers. The experiment results show that the gesture recognition rate of 20 different signs, performed by 8 different signers, is 85.67%.
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