Unposed Object Recognition using an Active Approach

Wallace Lawson, J. Gregory Trafton

Abstract

Object recognition is a practical problem with a wide variety of potential applications. Recognition becomes substantially more difficult when objects have not been presented in some logical, “posed” manner selected by a human observer. We propose to solve this problem using active object recognition, where the same object is viewed from multiple viewpoints when it is necessary to gain confidence in the classification decision. We demonstrate the effect of unposed objects on a state-of-the-art approach to object recognition, then show how an active approach can increase accuracy. The active approach works by attaching confidence to recognition, prompting further inspection when confidence is low. We demonstrate a performance increase on a wide variety of objects from the RGB-D database, showing a significant increase in recognition accuracy.

References

  1. Barbara, D., Domeniconi, C., and Rodgers, J. (2006). Detecting outliers using transduction and statistical testing. 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  2. Browatzki, B., Tikhanoff, V., Metta, G., Bulthoff, H., and Wallraven, C. (2012). Active object recognition on a humanoid robot. In IEEE International Conference on Robotics and Automation.
  3. Cyr, C. and Kimia, B. (2004). A similarity-based aspectgraph approach to 3d object recognition. International Journal of Computer Vision, 57(1).
  4. D. Wilkes, J. T. (1992). Active object recognition. In Computer Vision and Pattern Recognition (CVPR).
  5. Denzler, J. and Brown, C. (2002). Information theoretic sensor data selection and active object recognition and state estimation. IEEE. Trans. on Pattern Analysis and Machine Intelligence, 24(2).
  6. Duda, R., Hart, P., and Stork, D. (2000). Pattern Classification. Wiley Interscience.
  7. Farshidi, F., Sirouspour, S., and Kirubarajan, T. (2009). Robust sequential view planning for object recognition using multiple cameras. Image and Vision Computing.
  8. Fei-Fei, L., Fergus, R., and Perona, P. (2004). Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In IEEE Conferece on Computer Vision and Pattern Recognition.
  9. Frank, M., Munakata, Y., Hazy, T., and O'Reilly, R. (2012). Computational Cognitive Neuroscience.
  10. Gonzalez, R. and Woods, R. (2007). Digital Image Processing. Prentice Hall.
  11. Jia, Z., Chang, Y., and Chen, T. (2010). A general boosting based framework for active object recognition. British Machine Vision Conference (BMCV).
  12. Kietzmann, T. C., Lange, S., and Riedmiller, M. (2009). ”computational object recognition: A biologically motivated approach”. Biol. Cybern.
  13. Lai, K., Bo, L., Ren, X., and Fox, D. (2011). A large-scale hierarchical multi-view rgb-d object dataset. In International Conference on Robotics and Automation.
  14. LaPorte, C. and Arbel, T. (2006). Efficient discriminant viewpoint selection for active bayesian recognition. International Journal of Computer Vision, 68(3):267- 287.
  15. Pinto, N., Cox, D., and DiCarlo, J. (2008). Why is realworld visual object recognition hard? PLoS Computational Biology, 4(1).
  16. Sebastian, T., Klein, P., and Kimia, B. (2004). Recognition of shapes by editing their shock graphs. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(5):550 -571.
Download


Paper Citation


in Harvard Style

Lawson W. and Trafton J. (2013). Unposed Object Recognition using an Active Approach . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 309-314. DOI: 10.5220/0004285503090314


in Bibtex Style

@conference{visapp13,
author={Wallace Lawson and J. Gregory Trafton},
title={Unposed Object Recognition using an Active Approach},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={309-314},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004285503090314},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Unposed Object Recognition using an Active Approach
SN - 978-989-8565-47-1
AU - Lawson W.
AU - Trafton J.
PY - 2013
SP - 309
EP - 314
DO - 10.5220/0004285503090314