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Tytuł artykułu

The Automatic Recognition of Isolated Sign Language Signs Based on Gesture Components and DTW Algorithm

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Języki publikacji
EN
Abstrakty
EN
Author presents sign language features that can provide the basis of the sign language automatic recognition systems. Using parameters like position, velocity, angular orientation, fingers bending and the conventional or derivative dynamic time warping algorithms classification of 95 signs from the AUSLAN database was performed. Depending on the number of parameters used in classification different accuracy values were obtained (defined as the ratio of correctly recognized gestures to all gestures from test set), with the highest value 87.7% for the case of classification based on all the features and the derivative dynamic time warping method.
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Strony
1--8
Opis fizyczny
Bibliogr. 17 poz., rys., wykr., tab.
Twórcy
  • AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatics and Biomedical Engineering
Bibliografia
  • [1] von Agris U., Knorr M., Kraiss K.-F., The Significance of Facial Features for Automatic Sign Language Recognition, Proceeding of: Automatic Face & Gesture Recognition, 2008. FG ‘08. 8th IEEE International Conference on. Amsterdam, Netherlands.
  • [2] Sacks O., Zobaczyć głos. Podróż do świata ciszy. Zysk i S-ka Wydawnictwo s.j., Poznań 2011.
  • [3] Liang R.-H., Ouhyoung M., A Real-time Continuous Gesture Recognition System for Sign Language, Third IEEE International Conference on Automatic Face and Gesture Recognition, Proceedings, 1998.
  • [4] Lichtenauer J. F., Hendriks E. A., M. Reinders J.T., Sign Language Recognition by Combining Statistical DTW and Independent Classification, IEEE Transactions on Pattern Analysis and Machine Inteligence, vol. 30, no. 11, 2008.
  • [5] Nguyen T.D., Ranganath S., Facial expressions in American sign language: Tracking and recognition, Pattern Recognition 45, 2012.
  • [6] Gweth Y. L., Plahl C., Ney H., Enhanced Continuous Sign Language Recognition using PCA and Neural Network Features, Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on. Providence, Rhode Island.
  • [7] http://www.kinectranslator.com/pl/technologia/, date of last visit: July 2013.
  • [8] Barczewska K., Drozd A., Folwarczny Ł., Rozpoznawanie gestów z wykorzystaniem czujników inercyjnych o 9 stopniach swobody - Gesture recognition based on 9DOF inertial sensor, Pomiary, Automatyka, Kontrola, vol. 59, 2013.
  • [9] Barczewska K., Drozd A., Comparison of methods for hand gesture recognition based on Dynamic Time Warping algorithm, Proceedings on FedCSiS, Kraków, September 2013.
  • [10] Akl A., Feng C., Valaee S., A Novel Accelerometer-Based Gesture Recognition System, Transactions on Signal Processing, IEEE, vol. 59, No. 12, December 2011.
  • [11] Hussain S.M.A., Harun-ur Rashid A.B.M.: User Independent Hand Gesture Recognition by Accelerated DTW, IEEE/OSA/IAPR International Conference on Informatics, Electronics & Vision, Proceedings, Bangladesh 2012.
  • [12] http://archive.ics.uci.edu/ml/datasets/Australian+Sign+Language+signs+%28High+Quality%29, date of the last visit: July 2013.
  • [13] Kadous M. W., Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series, PhD Thesis (draft), School of Computer Science and Engineering, University of New South Wales, 2002.
  • [14] Helwig N. E., Hong S., Hsiao-Wecksler T., Time-Normalization Techniques for Gait Data, 33rd Annual Meeting of American Society of Biomechanics Materials, State College, PA, USA, 2009.
  • [15] Müller M.: Information Retrieval for Music and Motion. Chapter 4: Dynamic Time Warping. Springer Verlag 2007.
  • [16] Tsiporkova E., Dynamic Time Warping Algorithm for. PPT presentation available at: http://www.psb.ugent.be/cbd/papers/gentxwarper/DTWAlgorithm.ppt, date of the last visit: July 2013.
  • [17] Keogh, E., Pazzani, M., Derivative Dynamic Time Warping. In First SIAM International Conference on Data Mining (SDM’2001), Chicago, USA.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-343b3e8d-a796-4f1c-961f-9579ac6838f0
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