Geometry-based Superpixel Segmentation - Introduction of Planar Hypothesis for Superpixel Construction

M.-A. Bauda, S. Chambon, P. Gurdjos, V. Charvillat

2015

Abstract

Superpixel segmentation is widely used in the preprocessing step of many applications. Most of existing methods are based on a photometric criterion combined to the position of the pixels. In the same way as the Simple Linear Iterative Clustering (SLIC) method, based on k-means segmentation, a new algorithm is introduced. The main contribution lies on the definition of a new distance for the construction of the superpixels. This distance takes into account both the surface normals and a similarity measure between pixels that are located on the same planar surface. We show that our approach improves over-segmentation, like SLIC, i.e. the proposed method is able to segment properly planar surfaces.

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Susstrunk, S. (2012). SLIC superpixels compared to state-of-the-art superpixel methods.
  2. Arbelaez, P., Maire, M., Fowlkes, C., and Malik, J. (2009). From contours to regions: An empirical evaluation. In IEEE Computer Vision and Pattern Recognition.
  3. Bartoli, A. (2007). A random sampling strategy for piecewise planar scene segmentation. In Computer Vision and Image Understanding.
  4. Bauda, M.-A., Chambon, S., Gurdgos, P., and Charvillat, V. (2015). Image quality assessment for photoconsistency evaluation on planar classification in urban scenes. In International Conference on Pattern Recognition Applications and Methods.
  5. Comaniciu, D. and Meer, P. (2002). Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5).
  6. Felzenszwalb, P. and Huttenlocher, D. (2004). Efficient graph-based image segmentation. In International Journal of Computer Vision.
  7. Gould, S., Fulton, R., and Koller, D. (2009). Decomposing a scene into geometric and semantically consistent regions. In IEEE International Conference on Computer Vision.
  8. Hartley, R. I. and Zisserman, A. (2004). Multiple view geometry in computer vision. Cambridge University Press.
  9. Hoiem, D., Efros, A., and Herbert, M. (2005). Geometric context from a single image. In IEEE International Conference on Computer Vision.
  10. Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., and Siddiqi, K. (2009). Turbopixels: Fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12):2290-2297.
  11. Mic?us?ík, B. and Kos?ecká, J. (2010). Multi-view superpixel stereo in urban environments. In International Journal of Computer Vision.
  12. Moore, A., Prince, S., Warrell, J., Mohammed, U., and Jones, G. (2008). Superpixel lattices. In IEEE Computer Vision and Pattern Recognition.
  13. Mori, G. (2005). Guiding model search using segmentation. In IEEE International Conference on Computer Vision.
  14. Ren, X. and Malik, J. (2003). Learning a classification model for segmentation. In IEEE International Conference on Computer Vision, volume 1, pages 10-17.
  15. Saxena, A., Sun, M., and Ng, A. (2008). Make3d: Depth perception from a single still image. In IEEE Transactions on Pattern Analysis and Machine Intelligence.
  16. Schick, A., Fischer, M., and Stiefelhagen, R. (2012). Measuring and evaluating the compactness of superpixels. In International Conference on Pattern Recognition.
  17. Wang, S., Lu, H., Yang, F., and Yang, M. (2011). Superpixel tracking. In IEEE International Conference on Computer Vision.
  18. Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E. (2004). Image quality assessment: From error visibility to structural similarity. In IEEE Transaction on Image Processing.
  19. Weikersdorfer, D., Gossow, D., and Beetz, M. (2012). Depth-adaptive superpixels. In 21st International Conference on Pattern Recognition.
  20. Wu, C. (2011). Visualsfm: A visual structure from motion system.
  21. Yang, J., Gan, Z., Gui, X., Li, K., and Hou, C. (2013). 3- D geometry enhanced superpixels for RGB-D data. In Advances in Multimedia Information ProcessingPCM.
  22. Z. Wang, Z. and Bovik, A. (2002). A universal image quality index. In IEEE Signal Processing Letters.
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Paper Citation


in Harvard Style

Bauda M., Chambon S., Gurdjos P. and Charvillat V. (2015). Geometry-based Superpixel Segmentation - Introduction of Planar Hypothesis for Superpixel Construction . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 227-232. DOI: 10.5220/0005354902270232


in Bibtex Style

@conference{visapp15,
author={M.-A. Bauda and S. Chambon and P. Gurdjos and V. Charvillat},
title={Geometry-based Superpixel Segmentation - Introduction of Planar Hypothesis for Superpixel Construction},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={227-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005354902270232},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Geometry-based Superpixel Segmentation - Introduction of Planar Hypothesis for Superpixel Construction
SN - 978-989-758-089-5
AU - Bauda M.
AU - Chambon S.
AU - Gurdjos P.
AU - Charvillat V.
PY - 2015
SP - 227
EP - 232
DO - 10.5220/0005354902270232