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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Nov 2017
Next issue: Feb 2018
Avg review time: 105 days


PUBLISHER

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

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


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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  3/2012 - 13

An Efficient Solution for Hand Gesture Recognition from Video Sequence

PRODAN, R.-C. See more information about PRODAN, R.-C. on SCOPUS See more information about PRODAN, R.-C. on IEEExplore See more information about PRODAN, R.-C. on Web of Science, PENTIUC, S.-G. See more information about  PENTIUC, S.-G. on SCOPUS See more information about  PENTIUC, S.-G. on SCOPUS See more information about PENTIUC, S.-G. on Web of Science, VATAVU, R.-D. See more information about VATAVU, R.-D. on SCOPUS See more information about VATAVU, R.-D. on SCOPUS See more information about VATAVU, R.-D. on Web of Science
 
Click to see author's profile on 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 (752 KB) | Citation | Downloads: 544 | Views: 2,278

Author keywords
human robot interaction, computer vision, robotic arm, gesture recognition, image processing

References keywords
recognition(7), robot(6), gesture(6), vatavu(4), processing(4), interaction(4), image(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 85 - 88
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03013
Web of Science Accession Number: 000308290500013
SCOPUS ID: 84865856673

Abstract
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Full text preview
The paper describes a system of hand gesture recognition by image processing for human robot interaction. The recognition and interpretation of the hand postures acquired through a video camera allow the control of the robotic arm activity: motion - translation and rotation in 3D - and tightening/releasing the clamp. A gesture dictionary was defined and heuristic algorithms for recognition were developed and tested. The system can be used for academic and industrial purposes, especially for those activities where the movements of the robotic arm were not previously scheduled, for training the robot easier than using a remote control. Besides the gesture dictionary, the novelty of the paper consists in a new technique for detecting the relative positions of the fingers in order to recognize the various hand postures, and in the achievement of a robust system for controlling robots by postures of the hands.


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

[1] Andrew D. Wilson, "Robust Vision-Based Detection of Pinching for One and Two-Handed Gesture Input", In Proceedings of ACM UIST '06, pp. 255-258, 2006.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 9]


[2] S.G. Pentiuc, R.D. Vatavu, R. Prodan, T.I. Cerlinca, "Mathematical Model for a Robot Arm Control System", Advances in Electrical and Computer Engineering, vol. 5(12), no. 1(23), pp. 91-95, 2005

[3] Park, Hye Sun and Kim, Eun Yi and Jang, Sang Su and Park, Se Hyun and Park, Min Ho and Kim, Hang Joon, "HMM-Based Gesture Recognition for Robot Control", Pattern Recognition And Image Analysis, vol. 3522, pp. 695-716, 2005.
[CrossRef]


[4] Seong-Whan Lee, "Automatic gesture recognition for intelligent human-robot interaction, 7th International Conference on Automatic Face and Gesture Recognition", FGR 2006, pp. 645-650, 2006

[5] Radu-Daniel Vatavu, "Interfaces That Should Feel Right: Natural Interaction with Multimedia Information", Recent Advances in Multimedia Signal Processing and Communications, Springer Studies in Computational Intelligence - Springer Berlin / Heidelberg, vol. 231, pp. 145-170, 2009.
[CrossRef] [SCOPUS Times Cited 5]


[6] Radu-Daniel Vatavu, Stefan-Gheorghe Pentiuc, "Interactive Coffee Tables: Interfacing TV within an Intuitive, Fun and Shared Experience", EuroITV 2008, pp. 183-187, 2008

[7] Regina Bernhaupt, Marianna Obrist, Astrid Weiss, Elke Beck, and Manfred Tscheligi, "Trends in the living room and beyond: results from ethnographic studies using creative and playful probing". Comput. Entertain (CIE). vol. 6, no. 1, article no. 5, 2008

[8] R. C. Gonzalez and R. E. Woods, "Digital Image Processing", Prentice-Hall, 2nd edition, 2002.

[9] William K. Pratt, "Digital Image Processing: PIKS Scientific Inside", 4th Ed., Wiley-Interscience, 2007.

[10] J. LaViola, "A survey of hand posture and gesture recognition techniques and technology", Technical Report CS-99-11, Department of Computer Science, Brown University, Providence RI, 1999.

[11] Mike Wu, Ravin Balakrishnan. "Multi-finger and whole hand gestural interaction techniques for multi-user tabletop displays". The 16-th Annual ACM Symposium on User interface Software and Technology, New York, pp. 193-202, 2003.
[CrossRef]


[12] R.D. Vatavu, S.G. Pentiuc, C. Chaillou, L. Grisoni, Samuel Degrande, "Visual Recognition of Hand Postures for Interacting with Virtual Environments", Advances in Electrical and Computer Engineering, vol. 6 (13), no. 2(26), pp. 55-58, 2006

[13] Kemp, C. C., Anderson, C. D., Nguyen, H., Trevor, A. J., Xu, Z., "A Point-and-Click Interface for the Real World: Laser Designation of Objects for Mobile Manipulation". In 3rd ACM/IEEE International Conference on Human-Robot Interaction, pp. 241-248, 2008
[CrossRef] [SCOPUS Times Cited 71]


[14] Sakamoto, D., Honda, K., Inami, M., Igarashi, T., "Sketch and Run, A Stroke-based Interface for Home Robots", In 27th International Conference on Human Factors in Computing Systems, pp. 197-200, 2009

[15] Malima, A., Ozgur, E., Cetin, M., "A Fast Algorithm for Vision-Based Hand Gesture Recognition for Robot Control", in Proceedings of IEEE 14-th Conf. on Signal Processing and Communications Applications, pp. 1-4, 2006.
[CrossRef] [SCOPUS Times Cited 81]


[16] E. Ganea, D. D. Burdescu, M. Brezovan, "New Method to Detect Salient Objects in Image Segmentation using Hypergraph Structure," Advances in Electrical and Computer Engineering, vol. 11, no. 4, pp. 111-116, 2011.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[17] D. Ristic-Durrant, S. M. Grigorescu, A. Graser, Z. Cojbasic, V. Nikolic, "Robust Stereo-Vision Based 3D Object Reconstruction for the Assistive Robot FRIEND," Advances in Electrical and Computer Engineering, vol. 11, no. 4, pp. 15-22, 2011.
[CrossRef] [Full Text] [Web of Science Times Cited 3] [SCOPUS Times Cited 5]


References Weight

Web of Science® Citations for all references: 32 TCR
SCOPUS® Citations for all references: 174 TCR

Web of Science® Average Citations per reference: 2 ACR
SCOPUS® Average Citations per reference: 10 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 2017-12-11 19:05 in 51 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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