A Novel 2.5D Feature Descriptor Compensating for Depth Rotation

Frederik Hagelskjær, Norbert Krüger, Anders Glent Buch

Abstract

We introduce a novel type of local image descriptor based on Gabor filter responses. Our method operates on RGB-D images. We use the depth information to compensate for perspective distortions caused by out-of-plane rotations. The descriptor contains the responses of a multi-resolution Gabor bank. Contrary to existing methods that rely on a dominant orientation estimate to achieve rotation invariance, we utilize the orientation information in the Gabor bank to achieve rotation invariance during the matching stage. Compared to SIFT and a recent also projective distortion compensating descriptor proposed for RGB-D data, our method achieves a significant increase in accuracy when tested on a wide-baseline RGB-D matching 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. Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. (2008). Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3):346-359.
  3. Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., and Fua, P. (2012). Brief: Computing a local binary descriptor very fast. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(7):1281-1298.
  4. 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.
  5. Daugman, J. G. (1985). Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. JOSA A, 2(7):1160-1169.
  6. 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.
  7. Gossow, D., Weikersdorfer, D., and Beetz, M. (2012). Distinctive texture features from perspective-invariant keypoints. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 2764-2767. IEEE.
  8. Granlund, G. (1978). In search of a general picture processing operator. Computer Graphics and Image Processing, 8:155-173.
  9. Holzer, S., Rusu, R. B., Dixon, M., Gedikli, S., and Navab, N. (2012). Adaptive neighborhood selection for realtime surface normal estimation from organized point cloud data using integral images. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages 2684-2689. IEEE.
  10. Hubel, D. H. and Wiesel, T. N. (1959). Receptive fields of single neurones in the cat's striate cortex. The Journal of physiology, 148(3):574-591.
  11. Ilonen, J., Kamarainen, J.-K., and Kalviainen, H. (2007). Fast extraction of multi-resolution gabor features. In Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on, pages 481-486. IEEE.
  12. Ke, Y. and Sukthankar, R. (2004). Pca-sift: A more distinctive representation for local image descriptors. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 2, pages II-506. IEEE.
  13. Lai, K., Bo, L., Ren, X., and Fox, D. (2011). A largescale hierarchical multi-view rgb-d object dataset. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1817-1824. IEEE.
  14. Leutenegger, S., Chli, M., and Siegwart, R. Y. (2011). Brisk: Binary robust invariant scalable keypoints. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2548-2555. IEEE.
  15. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  16. Mikolajczyk, K. and Schmid, C. (2004). Scale & affine invariant interest point detectors. International Journal of Computer Vision, 60(1):63-86.
  17. Mikolajczyk, K. and Schmid, C. (2005). A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615-1630.
  18. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., and Van Gool, L. (2005). A comparison of affine region detectors. International journal of computer vision, 65(1-2):43- 72.
  19. Ojala, T., Pietikäinen, M., and Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971-987.
  20. Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011). ORB: an efficient alternative to SIFT or SURF. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2564-2571. IEEE.
  21. Schmid, C. and Mohr, R. (1997). Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5):530-534.
  22. Wiskott, L., Fellous, J.-M., Kuiger, N., and Von Der Malsburg, C. (1997). Face recognition by elastic bunch graph matching. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7):775-779.
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Paper Citation


in Harvard Style

Hagelskjær F., Krüger N. and Buch A. (2017). A Novel 2.5D Feature Descriptor Compensating for Depth Rotation . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 158-166. DOI: 10.5220/0006123201580166


in Bibtex Style

@conference{visapp17,
author={Frederik Hagelskjær and Norbert Krüger and Anders Glent Buch},
title={A Novel 2.5D Feature Descriptor Compensating for Depth Rotation},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={158-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006123201580166},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - A Novel 2.5D Feature Descriptor Compensating for Depth Rotation
SN - 978-989-758-225-7
AU - Hagelskjær F.
AU - Krüger N.
AU - Buch A.
PY - 2017
SP - 158
EP - 166
DO - 10.5220/0006123201580166