Integrating surface-based hypotheses and manipulation for autonomous segmentation and learning of object representations

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2012, Pages 1709-1715

  Ude, Aleš ^a,b   Schiebener, David ^a   Sugimoto, Norikazu ^b   Morimoto, Jun ^b  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION);

BAG OF FEATURES; INITIAL HYPOTHESIS; OBJECT REPRESENTATIONS; STATE OF THE ART; STATISTICAL CLASSIFIER; SURFACE-BASED; VISUAL FEATURE;

ROBOTS;

EID: 84864449616     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICRA.2012.6224641     Document Type: Conference Paper

References (25)

1. T. Chaperon and F. Goulette. Extracting cylinders in full 3d data using a random sampling method and the gaussian image. In Proc. Vision Modeling and Visualization Conference, 2001.
2. G. Csurka, C. Dance, L. X. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In Proc. ECCV Int. Workshop on Statistical Learning in Computer Vision, Prague, Czech Republic, 2004.
3. J. Feldman. What is a visual object? Trends in Cognitive Sciences, 7(6):252-256, 2003.
4. M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. In Communications of the ACM, volume 24, 1981.
5. P. Fitzpatrick. First contact: an active vision approach to segmentation. In Proc. 2003 IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pages 2161-2166, Las Vegas, Nevada, 2003.
6. P. Forssen. Maximally stable colour regions for recognition and matching. In IEEE Conf. Computer Vision and Pattern Recognition, 2007.
7. J. Gibson. The Ecological Approach to Visual Perception. Houghton Mifflin, Boston, MA, 1979.
8. C. Harris and M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, page 147151, 1988.
9. A. J. Ijspeert, J. Nakanishi, and S. Schaal. Movement imitation with nonlinear dynamical systems in humanoid robots. In Proc. IEEE Int. Conf. Robotics and Automation, pages 1398-1403, Washington, DC, 2002.
10. J. Kenney, T. Buckley, and O. Brock. Interactive segmentation for manipulation in unstructured environments. In Proc. IEEE Int. Conf. Robotics and Automation, pages 1377-1382, Kobe, Japan, 2009.
11. D. Kraft, N. Pugeault, E. Baseski, M. Popovic, D. Kragic, S. Kalkan, F. Wörgötter, and N. Krüger. Birth of the object: Detection of objectness and extraction of object shape through object-action complexes. Int. J. Humanoid Robot., 5(2):247-265, 2008.
12. M. Krainin, P. Henry, X. Ren, and D. Fox. Manipulator and object tracking for in-hand 3D object modeling. Int. J. Robotics Res., 2011 (online first).
13. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 2169- 2178, New York, NY, 2006.
14. W. H. Li and L. Kleeman. Segmentation and modeling of visually symmetric objects by robot actions. Int. J. Robotics Res., 30(9):1124-1142, 2011.
15. D. G. Lowe. Object recognition from local scale-invariant features. In Proc. Int. Conf. Computer Vision, Corfu, Greece, 1999.
16. J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. In Proc. British Machine Vision Conference, 2002.
17. D. Pelleg and A. Moore. X-means: Extending k-means with efficient estimation of the number of clusters. In Proc. 17th Int. Conf. Machine Learning, San Francisco, CA, 2000.
18. A. Ramisa, S. Vasudevan, D. Scaramuzza, R. L. de Mántaras, and R. Siegwart. A tale of two object recognition methods for mobile robots. In Proc. 6th Int. Conf. Computer Vision Systems, 2008.
19. C. E. Rasmussen and C. Williams. Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, 2006.
20. S. Schaal and D. Sternad. Programmable pattern generators. In Proc. Int. Conf. on Computational Intelligence in Neuroscience, Research Triangle Park, NC, 1998.
21. E. Stergaršek-Kuzmič and A. Ude. Object segmentation and learning through feature grouping and manipulation. In Proc. IEEE-RAS Int. Conf. on Humanoid Robots, pages 371-378, Nashville, TN, 2010.
22. A. Ude, D. Omrčen, and G. Cheng. Making object learning and recognition an active process. Int. J. Humanoid Robot., 5(2):247-265, 2008.
23. A. Ude and E. Oztop. Active 3-D vision on a humanoid head. In Proc. 14th Int. Conf. Advanced Robotics, Munich, Germany, 2009.
24. K. van de Sande, T. Gevers, and C. Snoek. Evaluating color descriptors for object and scene recognition. In IEEE Trans. Pattern Analysis and Machine Intelligence, pages 1582-1596, 2010.
25. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: A comprehensive study. Int. J. Comput. Vision, 73(2):123-138, 2007.