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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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  1/2008 - 11

User Head Movement Recognition and Interpretation System for Computer Interaction

UNGUREAN, C. O. See more information about UNGUREAN, C. O. on SCOPUS See more information about UNGUREAN, C. O. on IEEExplore See more information about UNGUREAN, C. O. 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 (715 KB) | Citation | Downloads: 840 | Views: 3,277

Author keywords
human computer interaction, gesture recognition, image processing, face recognition, Haar-like features, camshift algorithm

References keywords
recognition(6), motion(6), pentiuc(4), intel(4), human(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2008-04-02
Volume 8, Issue 1, Year 2008, On page(s): 62 - 66
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2008.01011
Web of Science Accession Number: 000259903500011
SCOPUS ID: 77955614730

Abstract
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Full text preview
The aim of the paper is to describe a system for the head gesture recognition developed in the frame of INTEROB project1. The goal of this project consists in developing an interaction based on gestures with information on robotic systems. In the paper we discussed a method for controlling the mouse pointer movements on the screen by recognizing the operator head movements captured by a video camera. In the second part of the paper it is described a fast and accurate method for hand posture recognition in video sequences.


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

[1] A. van Dam, "Post-WIMP user interfaces", Communications of the ACM, Vol. 40, No. 2, Pages 63-67, Feb. 1997.
[CrossRef] [Web of Science Times Cited 110]


[2] S. Oviatt and W. Wahlster (eds.), Human-Computer Interaction (Special Issue on Multimodal Interfaces), Lawrence Erlbaum Associates, Volume 12, Numbers 1 & 2, 1997.

[3] R. D. Vatavu, S.G. Pentiuc, C. Chaillo, On Natural Gestures for Interacting in Virtual Environments Advances in Electrical and Computer Engineering, Suceava, Romania ISSN 1582-7445, No 2/2005, volume 5 (12), pp. 72-79.

[4] G. Mahalu, R. Pentiuc, "Acquisition and Processing System for the Photometry Parameters of The Bright Objects", Advances in Electrical and Computer Engineering, Suceava, Romania, ISSN 1582-7445, No 1/2001, volume 1 (8), pp. 26-31.

[5] Pentiuc, S. G., Vatavu, R., Cerlinca, T. I., and Ungureanu, O. "Methods and Algoritms for Gestures Recognition and Understanding", The Eighth All-Ukrainian International Conference, UkrOBRAZ'2006, pp. 15-18, Ukraine, August 2006.

[6] Keates S., Perricos C., "Gesture as a means of computer access", Communication Matters. 10. 1, pp. 17-19

[7] P. Viola and M. J. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features", Proceedings of IEEE Computer Society's Computer Vision and Pattern Recognition (CVPR 2001), Vol. 1, pp. 511-518, 2001.

[8] R. Lienhart, J. Maydt, "An Extended Set of Haar-like Features for Rapid Object Detection", Intel Labs, 2002, Intel

[9] G. R. Bradski, "Computer video face tracking for use in a perceptual user interface". Intel Technology Journal, Q2 1998.

[10] D. Comaniciu and P. Meer, "Robust Analysis of Feature Spaces: Color Image Segmentation," CVPR'97, pp. 750-755.

[11] T. S. Caetano, D. A. C. Barone, "A probabilistic model for human skin color", IAP Conf. 2001, pp. 279-283.

[12] Leslie G. Farkas, Jeffrey C. Posnick, Tania M. Hreczko, "Anthropometric Growth Study of the Head", In: The Cleft Palate-Craniofacial Journal: Vol. 29, No. 4, pp. 303-308, 1992.
[CrossRef] [SCOPUS Times Cited 60]


[13] [Online] Available: Temporary on-line reference link removed - see the PDF document

[14] Polana R., Nelson R., "Low level recognition of human motion", In:Workshop on Motion of Nonrigid and Articulated Objects, pp. 77-82, 1994.
[CrossRef]


[15] Black M., Yacoob Y., "Tracking and recognizing rigid and nonrigid facial motions using local parametric model of image motion", In: Proceedings International Conference Computer Vision, pp 374-381. 1995.

[16] Madabhushi A., Aggarwal J., "A Bayesian approach to human activity recognition", In: Proceedings of IEEE Workshop on Visual Surveillance, pp. 25-32. 1999.

[17] Chen, F. S., Fu, C. M., Huang, C. L., "Hand gesture recognition using a real-time tracking method and hidden Markov models", IVC(21), No.8, August 2003, pp. 745-758.

[18] Cutler R., Davis L., "Robust real-time periodic motion detection, analysis, and applications", IEEE Trans Pattern Anal Mach Intel 22(8):781-796, 2000.
[CrossRef] [SCOPUS Times Cited 451]


[19] Gary R. Bradski, James W. Davis, "Motion segmentation and pose recognition with motion history gradients", Machine Vision and Applications, 2002, 13:174-184.
[CrossRef] [SCOPUS Times Cited 190]


[20] M. Cerlinca, A. Graur, S. G. Pentiuc, "Simulation of Switch Box Routing in FPGA", Advances in Electrical and Computer Engineering, Suceava, Romania, ISSN 1582-7445, No 1/2002, volume 2 (9), pp. 86-90.

References Weight

Web of Science® Citations for all references: 110 TCR
SCOPUS® Citations for all references: 701 TCR

Web of Science® Average Citations per reference: 6 ACR
SCOPUS® Average Citations per reference: 35 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 2017-12-13 09:05 in 38 seconds.




Note1: Web of Science® is a registered trademark of Thomson Reuters.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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