A Joint Segmentation and Classification of Object Shapes with Feedback for 3D Point Clouds

Frauke Wübbold, Bernardo Wagner

Abstract

Limited knowledge and limited deduction abilities are among the main restraints of autonomous robots for acting truly autonomously. This especially becomes obvious in the area of object recognition and classification, where many methods rely on knowledge teached manually in a prior setup step. Self-generating this knowledge from environment perception with a set of rules would significantly increase the robots autonomy as well as supersede manual training effort. In this paper, we propose a novel approach to rule-based classification for 3D point clouds by means of object shape, which additionally overcomes typical problems from a separate prior segmentation by integrating classification feedback into the segmentation process. Although it is still in its conceptual state, we explain in detail why we consider this approach to be very promising.

References

  1. Chen, Z. and Ellis, T. (2011). Multi-shape descriptor vehicle classification for urban traffic. In Proceedings of Digital Image Computing: Techniques and Applications. IEEE.
  2. Endres, F., Plagemann, C., Stachniss, C., and Burgard, W. (2009). Unsupervised discovery of object classes from range data using latent dirichlet allocation. In Proceedings of Robotics: Science and Systems. The MIT Press.
  3. Farmer, M. and Jain, A. (2004). A wrapper-based approach to image segmentation and classification. In Transactions on Image Processing Volume 14 Issue 12. IEEE.
  4. Guyet, T., Garbay, C., and Dojat, M. (2007). A humanmachine cooperative approach for time series data interpretation. In Proceedings on Artificial Intelligence in Medicine. IEEE.
  5. Huhle, B., Magnusson, M., Strasser, W., and Lilienthal, A. (2008). Registration of colored 3d point clouds with a kernel-based extension to the normal distributions transform. In Proceedings on Robotics and Automation. IEEE.
  6. Knopp, J., Prasad, M., and Van Gool, L. (2010). Orientation invariant 3d object classification using hough transform based methods. In Proceedings of the ACM workshop on 3D object retrieval. ACM.
  7. Lai, K. and Fox, D. (2009). 3D laser scan classification using web data and domain adaptation. In Proceedings of Robotics: Science and Systems. The MIT Press.
  8. Lecumberry, F., Pardo, A., and Sapiro, G. (2010). Simultaneous object classification and segmentation with high-order multiple shape models. In Transactions on Image Processing Volume 19 Issue 3. IEEE.
  9. Marton, Z.-C., Pangercic, D., Blodow, N., and Beetz, M. (2011). Combined 2d-3d categorization and classification for multimodal perception systems. In International Journal of Robotics Research. SAGE Publications.
  10. Schwartzkopf, W., Bovik, A., and Evans, B. (2005). Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images. In Transactions on Medical Imaging Volume 24 Issue 12. IEEE.
  11. Triebel, R., Shin, J., and Siegwart, R. (2010). Segmentation and unsupervised part-based discovery of repetitive objects. In Proceedings Robotics: Science and Systems. The MIT Press.
Download


Paper Citation


in Harvard Style

Wübbold F. and Wagner B. (2012). A Joint Segmentation and Classification of Object Shapes with Feedback for 3D Point Clouds . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8565-22-8, pages 388-393. DOI: 10.5220/0004121703880393


in Bibtex Style

@conference{icinco12,
author={Frauke Wübbold and Bernardo Wagner},
title={A Joint Segmentation and Classification of Object Shapes with Feedback for 3D Point Clouds},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2012},
pages={388-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004121703880393},
isbn={978-989-8565-22-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - A Joint Segmentation and Classification of Object Shapes with Feedback for 3D Point Clouds
SN - 978-989-8565-22-8
AU - Wübbold F.
AU - Wagner B.
PY - 2012
SP - 388
EP - 393
DO - 10.5220/0004121703880393