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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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  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: 1,584 | Views: 5,231

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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Full text preview
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.
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[CrossRef] [Web of Science Times Cited 17]


[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]


[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.
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[5] G. Mester, A. Rodic, Autonomous Locomotion of Humanoid Robots in Presence of Mobile and Immobile Obstacles, Towards Intelligent Engineering and Information Technology, Studies in Computational Intelligence, Volume 243, 2009.
[CrossRef]


[6] T. Asfour, P. Azad, N. Vahrenkamp, K. Regenstein, A. Bierbaum, K. Welke, J. Schroder, R. Dillmann, Toward humanoid manipulation in human-centered environments, Robotics and Autonomous Systems, vol. 56, no. 1, pp. 54-65, 2008.
[CrossRef] [Web of Science Times Cited 79]


[7] D. G. Lowe, "Distinctive image features from scale-invariant keypoints", Int. Journal of Computer Vision, vol. 60, no. 2, 2004.
[CrossRef] [Web of Science Times Cited 34257]


[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]


[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 7]


[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]


[12] D. Ojdanic, A. Graeser, "Improving the trajectory quality of a 7 DoF manipulator", in Proc. of the Robotik Conf., Munich, Germany, 2008.

[13] S. K. Vuppala, S. M. Grigorescu, D. Ristic-Durrant, A. Graeser, "Robust color object recognition for a service robotic task in the system FRIEND II", in Proc. of the IEEE 10th Int. Conf. on Rehabilitation Robotics ICORR, Noordwijk, Netherlands, 2007.
[CrossRef] [Web of Science Times Cited 3]


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


[19] S. K. Vuppala, A. Graeser, "An approach for tracking the 3d object pose using two object points", in Proc. of the Int. Conf. on Vision Systems ICVS, Santorini, Greece, 2008.
[CrossRef]


[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]


[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]


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


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[CrossRef] [Full Text] [Web of Science Times Cited 10]


References Weight

Web of Science® Citations for all references: 34,504 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 1,438 ACR
SCOPUS® Average Citations per reference: 0

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 2024-04-16 08:40 in 110 seconds.




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


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