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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Dynamic Gesture Recognition—A Machine Vision Based Approach

verfasst von : N. S. Sreekanth, N. K. Narayanan

Erschienen in: Proceedings of the International Conference on Signal, Networks, Computing, and Systems

Verlag: Springer India

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Abstract

Computationally simple method for dynamic hand gesture recognition is presented in this paper. The segmentation of hand which take parts in gesture production is being addressed tow different ways using color band based segmentation algorithms. The first one uses the special color stickers as part of finger and the second one does segmentation based on normal skin color. The movement of hand is tracked and Freeman’s eight directional code is generated corresponds to each gestures. A dynamic time wrapping based Levenshtein minimum edit distance algorithm is used for classification. The results of dynamic hand gestures with special color approach and without special color are discussed separately. The accuracy of the system is found to be more for special colour based segmentation than skin colour based segmentation techniques.

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Literatur
1.
Harshith.C, Karthik.R.Shastry, Manoj Ravindran, M.V.V.N.S Srikanth, Navee Lakshmikhanth “Survey on various Gesture Recognition Techniques for Interfacing Machines based on Ambient Intelligence” (IJCSES) Vol.1, No.2, November 2010.
2.
PragatiGarg, Naveen Aggarwal and SanjeevSofat, 2009. Vision Based Hand Gesture Recognition, World Academy of Science, Engineering and Technology 49, pp. 972–977.
3.
Laura Dipietro, Angelo M. Sabatini, and Paolo Dario, 2008. Survey of Glove-Based Systems and their applications, IEEE Transactions on systems, Man and Cybernetics, Part C: Applications and reviews, vol. 38(4), pp. 461–482, doi:10.​1109/​TSMCC.​2008.​923862.
4.
Jayshree R. Pansare, Kirti S. Rampurkar, Pritam L. Mahamane, Reshma J. Baravkar, Sneha V. Lanjewar” Real-Time Static Devnagri Sign Language Translation using Histogram “ International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 4, April 2013, pp. 1455–1459.
5.
Francis Quek, David McNeilly, Robert Bryll, Susan Duncan, Xin-Feng Ma, Cemil Kirbas, Karl E. McCulloughy, and Rashid Ansari: Gesture and Speech multimodal conversational interaction. ACM Transactions on Computer-Human Interaction, Vol. 9, No. 3, September 2002, pp. 171–193.
6.
Hairong Jiang Duerstock, B.S.; Wachs, J.P., “A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments”, Systems, Man, and Cybernetics: Systems, IEEE Transactions on (Volume:44, Issue: 5) May 2014, pp. 630–641.
7.
8.
Fabio Dominio, Mauro Donadeo, Pietro Zanuttigh. ―Combining multiple depth-based descriptors for hand gesture recognition‖ .Elsevier Publications, Pattern recognitions 2013.
9.
Lei Yang, Hui Li, Xiaoyu Wu, Dewei Zhao, Jun Zhai. ― An algorithm of skin detection based on texture‖. IEEE Image and Signal Processing(CSIP), 2011.
10.
Ohknishi, A. Nishikawa, Curvature-based segmentation and recognition of hand gestures, Proceedings Annual Conference On Robotics Society of Japan, 1997, pp. 401–407.
11.
Feng-Sheng Chen, Chih-Ming Fu, Chung-Lin Huang “Hand gesture recognition using a real-time tracking method and hidden Markov models”, Image and Vision Computing 21 (2003)-Elsewhere pp. 745–758.
12.
B.J Manikandan, Gowri Shankar, V Anoop, A Datta, V S Chakravarthy: LEKHAK: A System for Online Recognition of Handwritten Tamil Characters. Proceeding of (ICON-2002) Vikas Publishing House Pvt.Ltd. pp. 285–291.
13.
Mingyu Chen, Ghassan AlRegib, Biing-Hwang Juang, “Feature Processing and Modeling for 6D Motion Gesture Recognition” IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 3, APRIL 2013, pp. 561–571.
14.
15.
Wöllert, Thomas (Dipl.-Inf. (FH)): About Portable Keyboards with Design and Implementation of a Prototype Using Computer Vision, Semester Thesis, Master of Science - Computer Graphics and Image Processing, Munich University of Applied Sciences, Germany. June-2006.
16.
Rafael. C Gonzalez, Richard. E Woods, Steven .L Eddin: Digital Image Processing 2/e Pearson Education –Third Indian Reprint -2005, pp. 251.
17.
PEER, P., KOVAC, J., AND SOLINA, F. 2003. Human skin colour cluster-ing for face detection. Proceeding of EUROCON 2003.
18.
B.J Manikandan, Gowri Shankar, V Anoop, A Datta, V S Chakravarthy: LEKHAK: A System for Online Recognition of Handwritten Tamil Characters. Proceeding of the International Conference on Natural Language Processing (ICON-2002) Vikas Publishing House Pvt. Ltd. pp. 285–291.
19.
Metadaten
Titel
Dynamic Gesture Recognition—A Machine Vision Based Approach
verfasst von
N. S. Sreekanth
N. K. Narayanan
Copyright-Jahr
2017
Verlag
Springer India
Buch
Proceedings of the International Conference on Signal, Networks, Computing, and Systems

Print ISBN: 978-81-322-3590-3

Electronic ISBN: 978-81-322-3592-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-81-322-3592-7_11

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