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Warianty tytułu
Języki publikacji
Abstrakty
In thise paper a very simple method of the visual information interpretation for recognition and navigation purposes is presented and discussed. The proposed method consists of six steps: image acquisition, edge detection, fast edge vectorization using a high number of short preliminary vectors, aggregation of the preliminary vectors into the form of final vectors. The next stages of the visualthe visual information interpretation for recognition and navigation purposes will be description of the objects’ shapes by means of the final vectors, object recognition and/or robot navigation on the base of comparison between actual shape description and templates memorized during the programming/training process, but they are not discussed in this paper. The main advantage of the proposed method is a simple and time-effective algorithm, which can be performed in real time also by a simple and cheap processor, working as a “brain” of the considered robot.
Słowa kluczowe
Rocznik
Tom
Strony
10--17
Opis fizyczny
Bibliogr. 18 poz., rys.
Twórcy
autor
autor
- AGH University of Science and Technology, al. Mickiewicza 30, Kraków, Poland, tel. +48-12-6173924, mjzachara@agh.edu.pl
Bibliografia
- [1] D. Burshka, D. Cobzas, Z. Dodds, G. Hager, M. Jagers, K. Yerex, "Recent methods for image based modeling and rendering". In: IEEE Conference on Virtual Reality, Baltimore Hotel, Los Angeles, 2003, pp. 55-66.
- [2] A. J. Davison, Mobile Robot Navigation Using Active Mision, PhD thesis, Department of Engineering Science, University of Oxford, UK, 1998.
- [3] A. J. Davison and D. W. Murray, "Simultaneous localization and map-building using active vision". In: Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, pp. 865-880.
- [4] G. N. DeSouza and A. C. Kak, "Vision for mobile robot navigation: A survey". In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, pp. 237-267.
- [5] A. Doucet, C. Andrieu and S. Godsill, On Sequential Monte Carlo Sampling Methods for Bayesian Filtering, Statistics and Computing, vol. 10, no. 3,2000, pp. 197-208.
- [6] M. Hań, T. Kanadę, Scene reconstruction from multiple uncalibrated views. Technicat Report CMU-RI-TR-00-09, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA, 2000.
- [7] G. Elidan, G. Heitz, D. Kolie, "Learning object shape: From drawings to images". In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2,2006, pp. 2064-2071.
- [8] L Gordon, D. G. Lowe, "Scene modeling, recognition and tracing with invariant image features". In: International Symposium on Mixed and Augmented Reality, Arlington, VA, 2004, pp. 110-119.
- [9] M. Heath, S. Sarkar, T. Sanocki, K.W. Bowyer, "A Robust Visual Method for Assessing the Relative Performance of Edge Detection Algorithms", In: IEEE Transactions on Pattern Analysis and Machin Intelligence, vol. 19,1997, pp.1338-1359.
- [10] Y. Jin and S. Geman. Context and hierarchy in a proba-bilistic image model. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2006,pp.2145-2152.
- [11] J. Knight, Towards Fully Autonomous Visual Navigation.PhD thesis, Robotics Research Group, Department of Engineering Science, University of Oxford, UK, 2002.
- [12] E. Malis, Survey of vision-based robot control, In: Proceedings of European NavalShip Design, Captain Computer Forum, ENSIETA, Brest, France, 2002.
- [13] K. Mikolajczyk, B. Leibe, B. Shiele, "Local features for object class recognition". In: Proceedings of IEEE International Conference on Computer Vision, vol. 2, 2005, pp.1792-1799.
- [14] C. Pantofaru, M. Hebert, A comparison of image segmen-tation algorithms. Technical Report CMU-RI-TR-05-40, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA,2005.
- [15] S. Se, D. G. Lowe, J. Little, "Vision-based mobile robot localization and mapping using scale-invariant features". In: Proceedings of IEEE International Conference on Robotics and Automation, Seoul, Korea, 2001, pp. 2051-2058.
- [16] 0. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, W. T. Freeman, "Discovering objects and their location in images". In: Proceedings of IEEE International Conference on Computer Vision, vol l, 2005, pp. 370-377.
- [17] R. Tadeusiewicz, M.R. Ogiela, Medical Image Under-standing Technology, Series: Studies in Fuzziness and Soft Computing, vol 156, Springer-Verlag, Berlin Heidelberg New York, 2004.
- [18] R. Tadeusiewicz, M.R. Ogiela, "Why Automatic Understanding?". In: B. Beliczynski et ai (Eds.): ICANNGA 2007, Part U, Lecture Notes on Computer Science, vol 4432, Springer-Verlag, Berlin Heidelberg New York, 2007, pp. 477-491.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ5-0020-0002