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Stereovision 3D type workspace mapping system architecture for transport devices

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One of the effects of the exponential growth universally available computing power is decrease of the hardware cost especially multimedia components. This trend combined with integration of image acquisition hardware for multimedia applications for personal computers, enable to create new kind of vision systems - stereovision. The system presented on this paper using two synchronized cameras with USB type interface. In order to obtain an exact copy of the object in 3D type space is necessary to gather the Information about projected points of the space onto the more than one image. The paper is focus on double camera stereovision system and calibration problem with image rectification. The target was achieved with use open source Computer Vision libraries and toolboxes. Practical implementation of presented basic stereovision system (based on two cameras) is addressed for transportation devices which are operating in fixed 3D type space and include for example: device movement trajectory controlling, possible events detecting and worh space of materia handling devices modeling. Paper contains the following key chapters: cameras calibration problem short discussion, single camera calibration approach, and stereovision system architecture and calibration approach.
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  • AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Mickiewicza Av. 30, 30-059 Kraków, Poland tel: +48 12 6173103, +48 12 6173104,fax: +48 12 6173531, szpytko@agh.edu.pl
Bibliografia
  • [1] Baha-Touzene, N., Larabi, S., Obstacle detection with stereo vision, IMAGE’09 Biskra, pp. 163-177, 2009.
  • [2] Bouguet, J. Y., Camera Calibration Toolbox for Matlab, Dokument elektroniczny dostępny na http://www.vision.caltech.edu/bouguetj/calib_doc/ (visited on May 30, 2010).
  • [3] Bradley, D., Heidrich, W., Binocular Camera Calibration Using Rectification Error, Proceedings of Canadian Conference on Computer and Robot Vision, pp. 1-8, Ottawa 2010.
  • [4] Brown, D. C., Decentering Distortion of Lenses, Photometric Engineering, Vol. 32, No. 3, pp. 444-462, 1966.
  • [5] Coifman, B., Beymer, D., McLauchlan, P., Malik, J., A real-time computer vision system for vehicle tracking and traffic surveillance, Transportation Research Part C 6, pp. 271-288, 1998.
  • [6] De Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O., Computational Geometry Algorithms and Application, Springer-Verlag, 1998.
  • [7] Di Stefano, L., Marchionni, M., Mattoccia, S., A PC-based real-time stereo vision system, Machine Graphics & Vision International Journal, Vol. 13, Iss. 3, pp. 197-220, 2004.
  • [8] Diamant, E., Lecture Notes in Computer Science, Vol. 4729, pp. 62-71, Springer, Berlin/Heidelberg 2007.
  • [9] Ding-Zhu, D., Hwang, F., Computing in Euclidean Geometry, 2nd edition, World Scientific Publishing Co., Singapore, 1998.
  • [10] Farag, A. A., Hassouna, M. S., El-Baz, A., Hushek, S., Real-time vision-based approach for probe tracking in operating room. International Congress Series, Vol. 1268, pp. 467-472, 2004.
  • [11] O’Rourke, J., Computional geometry in C, 2nd edition, Cambridge University Press, 1998.
  • [12] Porikli, F. M., Divakaran, A., Multi-Camera Calibration, Object Tracking and Query Qeneration, IEEE International Conference on Multimedia and Expo (ICME), Vol. 1, pp. 653-656, 2003.
  • [13] Qian, Y., Helder Arau, J., Hong, W., A Stereovision Method for Obstacle Detection and Tracking in Non-Flat Urban Environments, Autonomous Robots, Vol. 19, pp. 141-157, 2005.
  • [14] Remondino, F., Fraser, C., Digital camera calibration methods: considerations and comparisons,ISPRS Commission V Symposium Image Engineering and Vision Metrology, IAPRS Vol. XXXVI, Part 5, pp. 266-272, Dresden 2006.
  • [15] Rzeszotarski, D., Strumiłło, P., Pełczyński, P., Więcek, B., Lorenc, A., System obrazowania stereoskopowego sekwencji scen trójwymiarowych, Zeszyty Naukowe Elektronika, Politechnika Łódzka, Nr 10, s. 165-184, 2005.
  • [16] Szpytko, J., Hyla, P., Reverse engineering vision technique applying in transport systems, Journal of KONES Powertrain and Transport, Vol. 16, No. 3, pp. 395-400, Warsaw 2009.
  • [17] Szpytko, J., Hyla, P., Work space supervising for material handling devices with machine vision assistance, Journal of KONBiN, Warszawa 2009.
  • [18] Zhang, Z., Flexible Camera Calibration by Viewing a Plane from Unknown Orientations, Seventh International Conference on Computer Vision ICCV’99, Vol. 1, pp. 666-674, 1999.
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Bibliografia
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bwmeta1.element.baztech-article-BUJ7-0018-0005
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