Motion based Segmentation for Robot Vision using Adapted EM Algorithm

Wei Zhao, Nico Roos

Abstract

Robots operate in a dynamic world in which objects are often moving. The movement of objects may help the robot to segment the objects from the background. The result of the segmentation can subsequently be used to identify the objects. This paper investigates the possibility of segmenting objects of interest from the background for the purpose of identification based on motion. It focusses on two approaches to represent the movements: one based on optical flow estimation and the other based on the SIFT-features. The segmentation is based on the expectation-maximization algorithm. A support vector machine, which classifies the segmented objects, is used to evaluate the result of the segmentation.

References

  1. Borshukov, G. D., Bozdagi, G., Altunbasak, Y., and Tekalp, A. M. (1997). Motion segmentation by multi-stage affine classification. IEEE Trans. Image Processing, 6:1591-1594.
  2. Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, pages 144-152. ACM.
  3. Bouguet, J.-Y. (2001). Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corporation, 5.
  4. Csurka, G., Dance, C., Fan, L., Willamowski, J., and Bray, C. (2004). Visual categorization with bags of keypoints. In Workshop on statistical learning in computer vision, ECCV, volume 1, pages 1-2. Prague.
  5. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), pages 1-38.
  6. Elhamifar, E. and Vidal, R. (2009). Sparse subspace clustering. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 2790- 2797. IEEE.
  7. Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395.
  8. Horn, B. K. and Schunck, B. G. (1981). Determining optical flow. In 1981 Technical Symposium East, pages 319- 331. International Society for Optics and Photonics.
  9. Karasulu, B. and Korukoglu, S. (2013). Moving object detection and tracking in videos. In Performance Evaluation Software, pages 7-30. Springer.
  10. Lowe, D. G. (1999). Object recognition from local scaleinvariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, volume 2, pages 1150-1157. Ieee.
  11. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  12. Lucas, B. D., Kanade, T., et al. (1981). An iterative image registration technique with an application to stereo vision. In IJCAI, volume 81, pages 674-679.
  13. Martinez-Conde, S., Macknik, S. L., and Hubel, D. H. (2004). The role of fixational eye movements in visual perception. Nature Neuroscience, 5:229 - 240.
  14. Narkhede, H. (2013). Review of image segmentation techniques. International Journal of Science and Modern Engineering (IJISME), 1:5461.
  15. Pan, Z. and Ngo, C.-W. (2005). Selective object stabilization for home video consumers. IEEE Trans. Consumer Electronics, 51(4):1074-1084.
  16. Rao, S. R., Tron, R., Vidal, R., and Ma, Y. (2008). Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE.
  17. Seerha, G. K. and Rajneet, K. (2013). Review on recent image segmentation techniques. International Journal on Computer Science and Engineering (IJCSE), 5:109-112.
  18. Selim, S. Z. and Ismail, M. A. (1984). K-means-type algorithms: a generalized convergence theorem and characterization of local optimality. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (1):81- 87.
  19. Shi, J. and Malik, J. (1998). Motion segmentation and tracking using normalized cuts. In Computer Vision, 1998. Sixth International Conference on, pages 1154-1160. IEEE.
  20. Wang, J. Y. and Adelson, E. H. (1994). Representing moving images with layers. Image Processing, IEEE Transactions on, 3(5):625-638.
  21. Wang, Y., Jodoin, P.-M., Porikli, F., Konrad, J., Benezeth, Y., and Ishwar, P. (2014). Cdnet 2014: An expanded change detection benchmark dataset. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, pages 393-400. IEEE.
  22. Weiss, Y. (1997). Smoothness in layers: Motion segmentation using nonparametric mixture estimation. In Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, pages 520-526. IEEE.
  23. Yan, J. and Pollefeys, M. (2006). A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and non-degenerate. In Computer Vision-ECCV 2006 , pages 94-106. Springer.
  24. Zappella, L., Llad ó, X., and Salvi, J. (2008). Motion segmentation: a review. In Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence, pages 398-407. IOS Press.
Download


Paper Citation


in Harvard Style

Zhao W. and Roos N. (2016). Motion based Segmentation for Robot Vision using Adapted EM Algorithm . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 649-656. DOI: 10.5220/0005721606490656


in Bibtex Style

@conference{visapp16,
author={Wei Zhao and Nico Roos},
title={Motion based Segmentation for Robot Vision using Adapted EM Algorithm},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={649-656},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005721606490656},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Motion based Segmentation for Robot Vision using Adapted EM Algorithm
SN - 978-989-758-175-5
AU - Zhao W.
AU - Roos N.
PY - 2016
SP - 649
EP - 656
DO - 10.5220/0005721606490656