Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2007 Article ID 87929 10 pages doi 2007 87929 Research Article Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition T. Shanableh1 and K. Assaleh2 1 Department of Computer Science College of Engineering American University of Sharjah P. O. Box 26666 Sharjah United Arab Emirates 2 Department of Electrical Engineering College of Engineering American University of Sharjah . Box 26666 Sharjah United Arab Emirates Received 9 January 2007 Revised 1 May 2007 Accepted 2 August 2007 Recommended by Thierry Pun This work introduces two novel approaches for feature extraction applied to video-based Arabic sign language recognition namely motion representation through motion estimation and motion representation through motion residuals. In the former motion estimation is used to compute the motion vectors of a video-based deaf sign or gesture. In the preprocessing stage for feature extraction the horizontal and vertical components of such vectors are rearranged into intensity images and transformed into the frequency domain. In the second approach motion is represented through motion residuals. The residuals are then thresholded and transformed into the frequency domain. Since in both approaches the temporal dimension of the video-based gesture needs to be preserved hidden Markov models are used for classification tasks. Additionally this paper proposes to project the motion information in the time domain through either telescopic motion vector composition or polar accumulated differences of motion residuals. The feature vectors are then extracted from the projected motion information. After that model parameters can be evaluated by using simple classifiers such as Fisher s linear discriminant. The paper reports on the classification accuracy of the proposed solutions. Comparisons with existing work reveal that
