Contour Localization based on Matching Dense HexHoG Descriptors

Yuan Liu, Paul Siebert

2014

Abstract

The ability to detect and localize an object of interest from a captured image containing a cluttered background is an essential function for an autonomous robot operating in an unconstrained environment. In this paper, we present a novel approach to refining the pose estimate of an object and directly labelling its contours by dense local feature matching. We perform this task using a new image descriptor we have developed called the HexHoG. Our key novel contribution is the formulation of HexHoG descriptors comprising hierarchical groupings of rotationally invariant (S)HoG fields, sampled on a hexagonal grid. These HexHoG groups are centred on detected edges and therefore sample the image relatively densely. This formulation allows arbitrary levels of rotation-invariant HexHoG grouped descriptors to be implemented efficiently by recursion. We present the results of an evaluation based on the ALOI image dataset which demonstrates that our proposed approach can significantly improve an initial pose estimation based on image matching using standard SIFT descriptors. In addition, this investigation presents promising contour labelling results based on processing 2892 images derived from the 1000 image ALOI dataset.

References

  1. Alahi, A., Ortiz, R., and Vandergheynst, P. (2012). Freak: Fast retina keypoint. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 510-517. IEEE.
  2. Borenstein, E. and Ullman, S. (2002). Class-specific, topdown segmentation. In Computer VisionECCV 2002, pages 109-122. Springer.
  3. Borji, A. and Itti, L. (2012). Exploiting local and global patch rarities for saliency detection. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 478-485. IEEE.
  4. Brown, M., Hua, G., and Winder, S. (2011). Discriminative learning of local image descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(1):43-57.
  5. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886- 893. IEEE.
  6. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010). Object detection with discriminatively trained part-based models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(9):1627-1645.
  7. Ferrari, V., Jurie, F., and Schmid, C. (2010). From images to shape models for object detection. International Journal of Computer Vision, 87(3):284-303.
  8. Geusebroek, J.-M., Burghouts, G. J., and Smeulders, A. W. (2005). The amsterdam library of object images. International Journal of Computer Vision, 61(1):103- 112.
  9. Kontschieder, P., Riemenschneider, H., Donoser, M., and Bischof, H. (2011). Discriminative learning of contour fragments for object detection. In BMVC, pages 1-12.
  10. Lazebnik, S., Schmid, C., and Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 2, pages 2169-2178. IEEE.
  11. Leibe, B., Leonardis, A., and Schiele, B. (2008). Robust object detection with interleaved categorization and segmentation. International journal of computer vision, 77(1-3):259-289.
  12. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  13. Mikolajczyk, K. and Schmid, C. (2005). A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615-1630.
  14. Murphy, K., Torralba, A., Eaton, D., and Freeman, W. (2006). Object detection and localization using local and global features. In Toward Category-Level Object Recognition, pages 382-400. Springer.
  15. Rosten, E. and Drummond, T. (2006). Machine learning for high-speed corner detection. In Computer VisionECCV 2006, pages 430-443. Springer.
  16. Schlecht, J. and Ommer, B. (2011). Contour-based object detection. In Proceedings of the British Machine Vision Conference. BVA Press.
  17. Shotton, J., Blake, A., and Cipolla, R. (2005). Contourbased learning for object detection. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, volume 1, pages 503-510. IEEE.
  18. Tola, E., Lepetit, V., and Fua, P. (2010). Daisy: An efficient dense descriptor applied to wide-baseline stereo. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(5):815-830.
  19. Xu, Y., Quan, Y., Zhang, Z., Ji, H., Fermuller, C., Nishigaki, M., and Dementhon, D. (2012). Contour-based recognition. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3402- 3409. IEEE.
  20. Yu, S. and Shi, J. (2003). Object-specific figure-ground segregation. In Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, volume 2, pages II-39. IEEE.
Download


Paper Citation


in Harvard Style

Liu Y. and Siebert P. (2014). Contour Localization based on Matching Dense HexHoG Descriptors . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 656-666. DOI: 10.5220/0004744006560666


in Bibtex Style

@conference{visapp14,
author={Yuan Liu and Paul Siebert},
title={Contour Localization based on Matching Dense HexHoG Descriptors},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={656-666},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004744006560666},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Contour Localization based on Matching Dense HexHoG Descriptors
SN - 978-989-758-003-1
AU - Liu Y.
AU - Siebert P.
PY - 2014
SP - 656
EP - 666
DO - 10.5220/0004744006560666