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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2013 - 12

3D Hand Gesture Recognition using the Hough Transform

OPRISESCU, S. See more information about OPRISESCU, S. on SCOPUS See more information about OPRISESCU, S. on IEEExplore See more information about OPRISESCU, S. on Web of Science, BARTH, E. See more information about BARTH, E. on SCOPUS See more information about BARTH, E. on SCOPUS See more information about BARTH, E. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (715 KB) | Citation | Downloads: 539 | Views: 2,991

Author keywords
image processing, computer vision, gesture recognition, Kinect camera, Hough transform

References keywords
gesture(11), recognition(10)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-08-31
Volume 13, Issue 3, Year 2013, On page(s): 71 - 76
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.03012
Web of Science Accession Number: 000326321600012
SCOPUS ID: 84884965434

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This paper presents an automatic 3D dynamic hand gesture recognition algorithm relying on both intensity and depth information provided by a Kinect camera. Gesture classification consists of a decision tree constructed on six parameters delivered by the Hough transform of projected 3D points. The Hough transform is originally applied, for the first time, on the projected gesture trajectories to obtain a reliable decision. The experimental data obtained from 300 video sequences with different subjects validate the proposed recognition method.

References | Cited By  «-- Click to see who has cited this paper

[1] A. Kolb, E. Barth, R. Koch, R. Larsen, "Time-of-Flight Cameras in Computer Graphics," In Computer Graphics Forum, 29(1), pp 141-159, 2010
[CrossRef] [Web of Science Times Cited 218] [SCOPUS Times Cited 178]

[2] X. Liu and K. Fujimura, "Hand gesture recognition using depth data," Proc. of the 6th IEEE international conf. on Automatic face and gesture recognition (FGR' 04), Washington, DC, USA, 529-534, 2004.

[3] S. Mitra and T. Acharya, "Gesture Recognition: A Survey," IEEE Trans. on Syst., man, and cybernetics, Part C: Applications and Reviews, pp. 311-324, vol. 37, no. 3, may 2007
[CrossRef] [Web of Science Times Cited 909] [SCOPUS Times Cited 1300]

[4] M. B. Holte, T. B. Moeslund, and P. Fihl, "View-invariant gesture recognition using 3D optical flow and harmonic motion context," Comput. Vis. Image Underst. 114, 12, pp. 1353-1361, 2010.
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 60]

[5] P. Doliotis, A. Stefan, C. McMurrough, D. Eckhard, and V. Athitsos, "Comparing gesture recognition accuracy using color and depth information," in Proceedings of PETRA, pp. 20:1-20:7, 2011.
[CrossRef] [SCOPUS Times Cited 65]

[6] C. Keskin, A. T. Cemgil, and L. Akarun, "DTW Based Clustering to Improve Hand Gesture Recognition," in Proceedings of HBU'11, pp. 72-81, Amsterdam, 2011.
[CrossRef] [SCOPUS Times Cited 20]

[7] L. Gallo, A.P. Placitelli, and M. Ciampi, "Controller-free exploration of medical image data: experiencing the Kinect," Proc. of. 24th IEEE CMBS'11, Piscataway, NJ, USA, 2011.
[CrossRef] [Web of Science Times Cited 106] [SCOPUS Times Cited 166]

[8] S. Soutschek, J. Penne and J. Hornegger, 3D gesture-based scene navigation in medical imaging applications using time-of-flight cameras, IEEE Conf. on Computer Vision & Pattern Recogn., Workshop on ToF-Camera based Computer Vision (2008).
[CrossRef] [SCOPUS Times Cited 57]

[9] P. Yanik et al., "Use of Kinect Depth Data and Growing Neural Gas for Gesture Based Robot Control," in Proc. of PervaSense, pp. 283-290, 2012.
[CrossRef] [SCOPUS Times Cited 19]

[10] Prodan, R.-C., Pentiuc, S.-G., Vatavu, R.-D., "An Efficient Solution for Hand Gesture Recognition from Video Sequence," Advances in Electrical and Computer Engineering, vol. 12, no. 3, pp. 85-88, 2012,
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 4]

[11] K. Lai, J. Konrad, and P. Ishwar, "A gesture-driven computer interface using Kinect camera," in Proc. Southwest Symposium on Image Analysis and Interpretation, Apr. 2012.

[12] Q. Munib, M. Habeeb, B. Takruri and H. A. Al-Malik, "American sign language (ASL) recognition based on Hough transform and neural networks," Expert Systems with Applications, vol. 32, 1, pp. 24-37, 2007.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 74]

[13] O. Altun, S. Albayrak, "Turkish fingerspelling recognition system using Generalized Hough Transform, interest regions, and local descriptors," Patt. Rec. Letters, vol. 32, 13, pp. 1626-1632, 2011.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 11]

[14] N.N. Bhat, "Real time robust hand gesture recognition and visual servoing," India Conference (INDICON), Annual IEEE, pp.1153-1157, 7-9 December 2012
[CrossRef] [SCOPUS Times Cited 1]

References Weight

Web of Science® Citations for all references: 1,338 TCR
SCOPUS® Citations for all references: 1,955 TCR

Web of Science® Average Citations per reference: 96 ACR
SCOPUS® Average Citations per reference: 140 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-01-20 12:47 in 81 seconds.

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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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