Local Single-Patch Features for Pose Estimation Using the Log-Polar Transform

  • Fredrik Viksten
  • Anders Moe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3522)


This paper presents a local image feature, based on the log-polar transform which renders it invariant to orientation and scale variations. It is shown that this feature can be used for pose estimation of 3D objects with unknown pose, with cluttered background and with occlusion. The proposed method is compared to a previously published one and the new feature is found to be about as good or better as the old one for this task.


Local Feature Cluttered Background Interest Point Detector Local Image Feature Query Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Chen, Q., Defrise, M., Deconinck, F.: Symmetric phase-only matched filtering of fourier-mellin transforms for image registration and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-16(12) (1994)Google Scholar
  2. 2.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8), 790–799 (1995)CrossRefGoogle Scholar
  3. 3.
    Edelman, S., Bulthoff, H.: Modeling human visual object recognition. In: Proc. International Joint Conference on Neural Networks, September 1992, vol. 4, pp. 37–42 (1992)Google Scholar
  4. 4.
    Fukunaga, K., Hostetler, L.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21(1), 32–40 (1975)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Granlund, G.H., Knutsson, H.: Signal Processing for Computer Vision. Kluwer Academic Publishers, Dordrecht (1995) ISBN 0-7923-9530-1Google Scholar
  6. 6.
    Granlund, G.H., Moe, A.: Unrestricted recognition of 3-D objects for robotics using multi-level triplet invariants. Artificial Intelligence Magazine 25(2), 51–67 (2004)Google Scholar
  7. 7.
    Harris, C.G., Stephens, M.: A combined corner and edge detector. In: 4th Alvey Vision Conference, September 1988, pp. 147–151 (1988)Google Scholar
  8. 8.
    Johansson, B., Moe, A.: Patch-duplets for object recognition and pose estimation. Technical Report LiTH-ISY-R-2553, Dept. EE, Linköping University, SE-581 83 Linköping, Sweden (November 2003)Google Scholar
  9. 9.
    Lindeberg, T.: Scale-space Theory in Computer Vision. Kluwer Academic Publishers, Dordrecht (1994) ISBN 0792394186Google Scholar
  10. 10.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. ICCV 1999 (1999)Google Scholar
  11. 11.
    Rivlin, E., Rotstein, H.: Control of a camera for active vision: Foveal vision, smooth tracking and saccade. IJCV 39(2), 81–96 (2000)zbMATHCrossRefGoogle Scholar
  12. 12.
    Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5), 530–535 (1997)CrossRefGoogle Scholar
  13. 13.
    Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int. Journal of Computer Vision 37(2), 151–172 (2000)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Fredrik Viksten
    • 1
  • Anders Moe
    • 1
  1. 1.Computer Vision LaboratoryLinköping UniversityLinköpingSweden

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