By Rudiger Dillmann, Jurgen Beyerer, Christoph Stiller, Marius Zollner, Tobias Gindele

ISBN-10: 3642102832

ISBN-13: 9783642102837

Das 21. Fachgespr?ch Autonome cellular Systeme (AMS 2009) ist ein discussion board, das Wissenschaftlerinnen und Wissenschaftlern aus Forschung und Industrie, die auf dem Gebiet der autonomen mobilen Systeme arbeiten, eine foundation f?r den Gedankenaustausch bietet und wissenschaftliche Diskussionen sowie Kooperationen auf diesem Forschungsgebiet f?rdert bzw. initiiert. Inhaltlich finden sich ausgew?hlte Beitr?ge zu den Themen Humanoide Roboter und Flugmaschinen, Perzeption und Sensorik, Kartierung und Lokalisation, Regelung, Navigation, Lernverfahren, Systemarchitekturen sowie der Anwendung von autonomen mobilen Systemen.

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Additional info for Autonome Mobile Systeme 2009 (21. Fachgesprach Karlsruhe, 3. 4. Dezember 2009) (Reihe: Informatik aktuell)

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In Proc. of the British Machine Vision Conference, BMVC, 2008. 10. L. Matthies, T. Kanade, and R. Szeliski. Kalman filter-based algorithms for estimating depth from image sequences. International Journal of Computer Vision, 3:209–238, 1989. 11. E. Rosten and T. T. Drummond. Machine learning for high-speed corner detection. In Proc. of the European Conference on Computer Vision, volume 1, pages 430–443, 2006. 12. Y. Yu, K. Wong, and M. Chang. A Fast Recursive 3D Model Reconstruction Algorithm for Multimedia Applications.

Drummond. Unified Loop Closing and Recovery for Real Time Monocular SLAM. In Proc. of the British Machine Vision Conference, BMVC, 2008. 10. L. Matthies, T. Kanade, and R. Szeliski. Kalman filter-based algorithms for estimating depth from image sequences. International Journal of Computer Vision, 3:209–238, 1989. 11. E. Rosten and T. T. Drummond. Machine learning for high-speed corner detection. In Proc. of the European Conference on Computer Vision, volume 1, pages 430–443, 2006. 12. Y. Yu, K. Wong, and M.

The SADs are computed for each sub-window Ci . The final correlation value C is formed by adding the correlation value Co of the central 8 × 8 sub-window and the values of the two best surrounding correlation windows Cb and Cs . This measure of similarity performs better near object boundaries since at least two sub-windows are located on a single object in most cases. Depending on the dominant image structure the correspondence is either attached to the foreground or the background object reducing the blur along the reconstructed object borders2.

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Autonome Mobile Systeme 2009 (21. Fachgesprach Karlsruhe, 3. 4. Dezember 2009) (Reihe: Informatik aktuell) by Rudiger Dillmann, Jurgen Beyerer, Christoph Stiller, Marius Zollner, Tobias Gindele


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