ROBUST ONLINE SEGMENTATION OF UNKNOWN OBJECTS FOR MOBILE ROBOTS

Xin Wang, Maja Rudinac, Pieter P. Jonker

2012

Abstract

In this paper we present a novel vision system for object-driven and online learning based segmentation of unknown objects in a scene. The main application of this system is for mobile robots exploring unknown environments, where unknown objects need to be inspected and segmented from multiple viewpoints. In an initial step, objects are detected using a bottom-up segmentation method based on salient information. The cluster with the most salient points is assumed to be the most dominant object in the scene and serves as an initial model for online segmentation. Then the dominant object is tracked by a Lucas-Kanade tracker and the object model is constantly updated and learned online based on Random Forests classifier. To refine the model a two-step object segmentation using Gaussian Mixture Models and graph cuts is applied. As a result, the detailed contour information of the dominant unknown object is obtained and can further be used for object grasping and recognition. We tested our system in very challenging conditions with multiple identical objects, severe occlusions, illumination changes and cluttered background and acquired very promising results. In comparison with other methods, our system works online and requires no input from users.

References

  1. Björkman, M. and Kragic, D. (2010). Active 3D scene segmentation and detection of unknown objects. In ICRA, pages 3114-3120.
  2. Boykov, Y. and Jolly, M.-P. (2001). Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, volume 1, pages 105 -112 vol.1.
  3. Bradski, G. R. (1998). Computer Vision Face Tracking For Use in a Perceptual User Interface. Intel Technology Journal, (Q2).
  4. Breiman, L. (2001). Random Forests. Machine Learning, 45:5-32.
  5. Comaniciu, D., Meer, P., and Member, S. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:603-619.
  6. Hou, X. and Zhang, L. (2007). Saliency detection: A spectral residual approach. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR07). IEEE Computer Society, pages 1-8.
  7. Itti, L., Koch, C., and Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254-1259.
  8. Kalal, Z., Matas, J., and Mikolajczyk, K. (2009). Online learning of robust object detectors during unstable tracking. In Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, pages 1417 -1424.
  9. Kalal, Z., Matas, J., and Mikolajczyk, K. (2010). P-N learning: Bootstrapping binary classifiers by structural constraints. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 0:49- 56.
  10. Kootstra, G., Bergström, N., and Kragic, D. (2010). Using symmetry to select fixation points for segmentation. In Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010, pages 3894-3897.
  11. Lepetit, V. and Fua, P. (2006). Keypoint Recognition Using Randomized Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28:1465-1479.
  12. Mairal, J., Bach, F., Ponce, J., and Sapiro, G. (2010). Online Learning for Matrix Factorization and Sparse Coding. J. Mach. Learn. Res., 11:19-60.
  13. Matas, J., Chum, O., Urban, M., and Pajdla, T. (2004). Robust wide-baseline stereo from maximally stable extremal regions. Image Vision Comput., 22(10):761- 767.
  14. Mooser, J., You, S., and Neumann, U. (2007). Real-Time Object Tracking for Augmented Reality Combining Graph Cuts and Optical Flow. In Mixed and Augmented Reality, pages 145 -152.
  15. Rasolzadeh, B., Björkman, M., Huebner, K., and Kragic, D. (2010). An active vision system for detecting, fixating and manipulating objects in the real world. The International Journal of Robotics Research, 29(2-3):133- 154.
  16. Rother, C., Kolmogorov, V., and Blake, A. (2004). ”GrabCut”: interactive foreground extraction using iterated graph cuts. In ACM SIGGRAPH 2004 Papers, SIGGRAPH 7804, pages 309-314, New York, NY, USA. ACM.
  17. Rudinac, M. and Jonker, P. P. (2010). Saliency detection and object localization in indoor environments. In Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010, pages 404-407.
  18. Tax, D. (2001). One-class classification. phd, Delft University of Technology, Delft.
  19. Vezhnevets, A. V. (2005). ”GrowCut”-Interactive MultiLabel N-D Image Segmentation By Cellular.
  20. Zivkovic, Z. (2004). Improved adaptive Gaussian mixture model for background subtraction. In Pattern Recognition, 2004. Proceedings of the 17th International Conference on, volume 2, pages 28 - 31 Vol.2.
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Paper Citation


in Harvard Style

Wang X., Rudinac M. and P. Jonker P. (2012). ROBUST ONLINE SEGMENTATION OF UNKNOWN OBJECTS FOR MOBILE ROBOTS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 365-374. DOI: 10.5220/0003866803650374


in Bibtex Style

@conference{visapp12,
author={Xin Wang and Maja Rudinac and Pieter P. Jonker},
title={ROBUST ONLINE SEGMENTATION OF UNKNOWN OBJECTS FOR MOBILE ROBOTS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={365-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003866803650374},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - ROBUST ONLINE SEGMENTATION OF UNKNOWN OBJECTS FOR MOBILE ROBOTS
SN - 978-989-8565-03-7
AU - Wang X.
AU - Rudinac M.
AU - P. Jonker P.
PY - 2012
SP - 365
EP - 374
DO - 10.5220/0003866803650374