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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

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


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  4/2011 - 3

 HIGH-IMPACT PAPER 

Robust Stereo-Vision Based 3D Object Reconstruction for the Assistive Robot FRIEND

RISTIC-DURRANT, D. See more information about RISTIC-DURRANT, D. on SCOPUS See more information about RISTIC-DURRANT, D. on IEEExplore See more information about RISTIC-DURRANT, D. on Web of Science, GRIGORESCU, S. M. See more information about  GRIGORESCU, S. M. on SCOPUS See more information about  GRIGORESCU, S. M. on SCOPUS See more information about GRIGORESCU, S. M. on Web of Science, GRASER, A. See more information about  GRASER, A. on SCOPUS See more information about  GRASER, A. on SCOPUS See more information about GRASER, A. on Web of Science, COJBASIC, Z. See more information about  COJBASIC, Z. on SCOPUS See more information about  COJBASIC, Z. on SCOPUS See more information about COJBASIC, Z. on Web of Science, NIKOLIC, V. See more information about NIKOLIC, V. on SCOPUS See more information about NIKOLIC, V. on SCOPUS See more information about NIKOLIC, V. on Web of Science
 
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Download PDF pdficon (574 KB) | Citation | Downloads: 1,667 | Views: 5,547

Author keywords
robust robot vision, feedback control in image processing, feature-based object recognition, neuro-fuzzy classification, assistive robot

References keywords
vision(8), systems(8), object(7), robots(6), robotics(6), graeser(6), system(5), robot(5), autonomous(5), robotic(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-11-30
Volume 11, Issue 4, Year 2011, On page(s): 15 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.04003
Web of Science Accession Number: 000297764500003
SCOPUS ID: 84856594148

Abstract
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A key requirement of assistive robot vision is the robust 3D object reconstruction in complex environments for reliable autonomous object manipulation. In this paper the idea is presented of achieving high robustness of a complete robot vision system against external influences such as variable illumination by including feedback control of the object segmentation in stereo images. The approach used is to change the segmentation parameters in closed-loop so that object features extraction is driven to a desired result. Reliable feature extraction is necessary to fully exploit a neuro-fuzzy classifier which is the core of the proposed 2D object recognition method, predecessor of 3D object reconstruction. Experimental results on the rehabilitation assistive robotic system FRIEND demonstrate the effectiveness of the proposed method.


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

[1] D. Kim, R. Lovelett, A. Behal, "An empirical study with simulated adl tasks using vision-guided assistive robot arm", in Proc. of the IEEE 11th Int. Conf. on Rehabilitation Robotics ICORR, Japan, 2009.
[CrossRef] [SCOPUS Times Cited 28]


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[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 19]


[3] S. Hussmann and T. Liepert, "Robot Vision System based on a 3D-TOF Camera", Instrumentation and Measurement Technology Conference-IMTC 2007, Warsaw, Poland, 2007.
[CrossRef] [SCOPUS Times Cited 31]


[4] S. Gaechter, A. Harati and R. Siegwart, "Incremental Object Part Detection toward Object Classification in a Sequence of Noisy Range Images", in Proc. of IEEE International Conference on Robotics and Automation ICRA 2008, 2008.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 12]


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[CrossRef] [SCOPUS Times Cited 4]


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[CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 102]


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[CrossRef] [Web of Science Times Cited 35377] [SCOPUS Times Cited 49007]


[8] D. Kragic, Bjoerman, H. Christensen, J.-O. Eklundh, "Vision for robotic object manipulation in domestic settings", Robotics and Autonomous Systems, vol. 52, pp. 85-100, 2005.
[CrossRef] [Web of Science Times Cited 60] [SCOPUS Times Cited 75]


[9] M. Sridharan, P. Stone, "Structure-based color learning on a mobile robot under changing illumination", Autonomous Robots Journal, vol. 23, pp. 161-182, 2007.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 15]


[10] O. Ivlev, C. Martens, A. Graeser, "Rehabilitation robots FRIEND-I and FRIEND-II with the dexterous lightweight manipulator", Restoration of Wheeled Mobility in SCI Rehabilitation, vol. 17, pp. 111-123, 2005.

[11] O. Prenzel, C. Martens, M. Cyriacks, C. Wang, A. Graeser, "System controlled user interaction within the service robotic control architecture MASSiVE", Robotica, Special Issue, vol. 25, no. 2, 2007.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 13]


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[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 12]


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[15] P. Sanz, M. R., J. Sanchez, "Including efficient object recognition capabilities in online robots: From a statistical to a neural-network classifier", IEEE Trans. on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 35, no. 1, pp. 87-96, 2005.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 16]


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[17] E. Dogantekin, M. Yilmaz, A. Dogantekin, E. Avci, A. Sengur, "A robust technique based on invariant moments - ANFIS for recognition of human parasite eggs in microscopic images", Expert Systems with Applications, vol. 35, no. 3, pp. 728-738, 2008.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 59]


[18] R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2004.
[CrossRef]


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[CrossRef] [SCOPUS Record]


[20] D. Ristic, Feedback structures in image processing, Ph.D. thesis, Shaker Verlag, Germany, 2007.

[21] D. Ristic, A. Graser, "Performance measure as feedback variable in image processing", EURASIP Journal on Applied Signal Processing, vol. 2006, 2006.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 10]


[22] S. M. Grigorescu, D. Ristic-Durrant, A. Graeser, "ROVIS: Robust machine Vision for Service robotic system FRIEND", in Proc. of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA, October, 2009.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 17]


[23] M. Sridharan, "Bootstrap Learning and Visual Processing Management on Mobile Robots", Advances in Artificial Intelligence, vol. 2010, 2010.
[CrossRef]


[24] C. Suliman, C. Cruceru, F. Moldoveanu, "Kalman filter based tracking in an video surveillance system", Advances in Electrical and Computer Engineering, vol. 10, no. 2, pp. 30-34, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 10] [SCOPUS Times Cited 12]


References Weight

Web of Science® Citations for all references: 35,627 TCR
SCOPUS® Citations for all references: 49,432 TCR

Web of Science® Average Citations per reference: 1,484 ACR
SCOPUS® Average Citations per reference: 2,060 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 2024-11-19 07:13 in 129 seconds.




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