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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Aug 2018
Next issue: Nov 2018
Avg review time: 82 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

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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-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.

Read More »


    
 

  3/2015 - 6

Automatic Assistant for Better Mobility and Improved Cognition of Partially Sighted Persons

TAPU, R. See more information about TAPU, R. on SCOPUS See more information about TAPU, R. on IEEExplore See more information about TAPU, R. on Web of Science, MOCANU, B. See more information about  MOCANU, B. on SCOPUS See more information about  MOCANU, B. on SCOPUS See more information about MOCANU, B. on Web of Science, ZAHARIA, T. See more information about ZAHARIA, T. on SCOPUS See more information about ZAHARIA, T. on SCOPUS See more information about ZAHARIA, T. 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 (1,707 KB) | Citation | Downloads: 279 | Views: 1,586

Author keywords
visual impaired navigation assistant, obstacle detection and classification, audio feedback, smartphone device

References keywords
visually(11), vision(11), impaired(10), system(5), detection(5), blind(5), visual(4), obstacle(4), navigation(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2015-08-31
Volume 15, Issue 3, Year 2015, On page(s): 45 - 52
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.03006
Web of Science Accession Number: 000360171500006
SCOPUS ID: 84940735074

Abstract
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In these paper we introduce a novel computer vision assistant for autonomous navigation of partially sighted people. We begin by detecting any type of static and dynamic obstacle present in the scene. Then, we introduce an adapted version of HOG (Histogram of Oriented Gradients) descriptor incorporated into the BoVW (Bag of Visual Words) retrieval framework and demonstrate how this combination can be used for obstacle classification. The design is completed with an acoustic feedback that alert user of potential hazards. The audio bone conduction is employed to allow the visually impaired to hear other sounds from the environment. At the hardware level, the system is totally integrated on a smartphone which makes it easy to wear, non-invasive and low-cost.


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

[1] D. Pascolini, S. P. Mariotti, "Global data on visual impairments 2010," World Health Organization, Geneva, 2012.

[2] B. B. Blasch, W. R. Wiener, and R. L. Welsh, "Foundations of Orientation and Mobility", 2nd New York: American Foundation for the Blind AFB Press, pp. 42-55, 1997.

[3] C. Shah, M. Bouzit, M. Youssef, and L. Vasquez, "Evaluation of RU-Netra Tactile Feedback Navigation System for the Visually Impaired," International Workshop on Virtual Rehabilitation, pp. 72-77, 2006.
[CrossRef]


[4] R. G. Golledge, J. R. Marston, and C. M. Costanzo, "Attitudes of visually impaired persons towards the use of public transportation," Journal of Visually Impairment Blindness, vol. 90, pp. 446-459, 1997.

[5] A. Rodriguez, J. J. Yebes, P. F. Alcantarilla, L. M. Bergasa, J. Almazan, and A. Cela, "Assisting the visually impaired: obstacle detection and warning system by acoustic feedback," Sensors, vol. 12, pp. 17476-17496, 2012.
[CrossRef] [SCOPUS Times Cited 72]


[6] D. Dakopoulos, N. G. Bourbakis, "Wearable obstacle avoidance electronic travel aids for blind: a survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol.40, no.1, pp.25-35, Jan. 2010.
[CrossRef] [Web of Science Times Cited 181] [SCOPUS Times Cited 261]


[7] J. A. Hesch, S. I. Roumeliotis, "Design and analysis of a portable indoor localization aid for the visually impaired," Journal of Robotics Research, pp. 1400-1415, 2010.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 34]


[8] J. M. Saez, F. Escolano, and A. PeƱalver, "First steps towards stereo based 6DOF SLAM for the visually impaired," IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp.23-23, June 2005.
[CrossRef]


[9] V. Pradeep, G. Medioni, J. Weiland, "Robot Vision for the Visually Impaired," IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.15-22, June 2010.
[CrossRef] [SCOPUS Times Cited 76]


[10] J. M. Saez, F. Escolano, "Stereo-Based Aerial Obstacle Detection for the Visually Impaired", In ECCV workshop. on Computer Vision Applications for the Visually Impaired, France 2008.

[11] J. M. Loomis, R. L. Klatzky, N. A. Giudice, "Sensory substitution of vision: importance of perceptual and cognitive processing", in Assistive Technology for Blindness and Low Vision, Eds. Boca Raton, pp. 161-192, 2013.

[12] P. B. L. Meijer, "An experimental system for auditory image representations", IEEE Trans. Biomedical Engineering, vol. 39(2), pp. 112-121, 1992.
[CrossRef] [Web of Science Times Cited 294] [SCOPUS Times Cited 432]


[13] J. L. Gonzalez-Mora, A. Rodriguez-Hernandez, L. F. Rodriguez-Ramos, L. Diaz-Saco, N. Sosa, "Development of a new space perception system for blind people, based on the creation of a virtual acoustic space", Lecture Notes in Computer Science Engineering Applications of Bio-Inspired Artificial Neural Networks, pp. 321-330, 1999.
[CrossRef] [SCOPUS Times Cited 41]


[14] S. Meers and K. Ward, "A substitute vision system for providing 3D perception and GPS navigation via electro-tactile stimulation," 1st Int. Conf. Sens. Technol., New Zealand, Nov. pp. 21-23, 2005.

[15] L. A. Johnson and C. M. Higgins, "A navigation aid for the blind using tactile-visual sensory substitution," in Proc. 28th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 6298-6292, 2006.
[CrossRef] [SCOPUS Times Cited 80]


[16] D. Dakopoulos and N. Bourbakis, "Preserving visual information in low resolution images during navigation for visually impaired," Proceedings of the 1st International Conference on PErvasive Technologies Related to Assistive Environments, Athens, Greece, pp. 1-6, 2008.
[CrossRef] [SCOPUS Times Cited 22]


[17] A. Khan, F. Moideen, J. Lopez, W. L. Khoo, Z. Zhu, "KinDetect: Kinect detecting objects", in Computer Helping people with special needs, vol. LNCS7382, pp. 588-595, 2012.

[18] E. Peng, P. Peursum, L. Li, S. Venkatesh, "A smartphone-based obstacle sensor for the visually impaired", Lecture Notes in Computer Science, Ubiquitous Intelligence and Computing, pp. 590-604, 2010.
[CrossRef] [SCOPUS Times Cited 1]


[19] R. Manduchi, "Mobile vision as assistive technology for the blind: An experimental study", Proceedings of the 13th International Conference on Computers Helping People with Special Needs, volume 2, pp. 9-16, Austria, 2012.
[CrossRef] [SCOPUS Times Cited 24]


[20] R. Tapu, T. Zaharia, "Salient object detection based on spatiotemporal attention models," IEEE International Conference on Consumer Electronics (ICCE), pp.39-42, Jan. 2013.
[CrossRef] [SCOPUS Times Cited 2]


[21] N. Dalal, B. Triggs, "Histograms of oriented gradients for human detection", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.1, pp.886-893, June 2005.
[CrossRef] [SCOPUS Times Cited 15516]


[22] N. Dalal, B. Triggs, "Object detection using histograms of oriented gradients", in European Conference on Computer Vision, vol. 1, pp 886-893, 2006.

[23] G. Csurka, C. R. Dance, L. Fan, J. Willamowski, C. Bray, "Visual categorization with bags of keypoints," In Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1-22, 2004.

[24] S. Tong, E. Chang, "Support Vector Machine Active Learning for Image Retrieval," Proceedings of the Ninth ACM International Conference on Multimedia., pp. 107-118, 2001.
[CrossRef]




References Weight

Web of Science® Citations for all references: 504 TCR
SCOPUS® Citations for all references: 16,561 TCR

Web of Science® Average Citations per reference: 20 ACR
SCOPUS® Average Citations per reference: 662 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 2018-10-22 21:33 in 113 seconds.




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


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