Advertisement

A Fast Probabilistic Model for Hypothesis Rejection in SIFT-Based Object Recognition

  • Patricio Loncomilla
  • Javier Ruiz-del-Solar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

This paper proposes an improvement over the traditional SIFT-based object recognition methodology proposed by Lowe [3]. This improvement corresponds to a fast probabilistic model for hypothesis rejection (affine solution verification stage), which allows a large reduction in the number of false positives. The new probabilistic model is evaluated in an object recognition task using a database of 100 pairs of images.

References

  1. 1.
    Ferrari, V., Tuytelaars, T., Van Gool, L.: Simultaneous Object Recognition and Segmentation by Image Exploration. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 40–54. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conf., Manchester, UK, pp. 147–151 (1988)Google Scholar
  3. 3.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  4. 4.
    Lowe, D.G.: Local Features View Clustering for 3D Object Recognition. In: Proc. of the IEEE Conf. on Comp. Vision and Patt. Recog., Hawai, Dic, pp. 682–688 (2001)Google Scholar
  5. 5.
    Loncomilla, P., Ruiz del Solar, J.: Improving SIFT-based Object Recognition for Robot Applications. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 1084–1092. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Loncomilla, P., Ruiz-del-Solar, J.: Gaze Direction Determination of Opponents and Teammates in Robot Soccer. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 230–242. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Loncomilla, P., Ruiz-del-Solar, J.: An improved SIFT-based Object Recognition Methodology, Tech. Report UCH-DIE-VISION-2006-03, Dept. of E. Eng., U. de Chile (2006)Google Scholar
  8. 8.
    Mikolajczyk, K., Schmid, C.: Scale & Affine Invariant Interest Point Detectors. Int. Journal of Computer Vision 60(1), 63–96 (2004)CrossRefGoogle Scholar
  9. 9.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(10), 1615–1630Google Scholar
  10. 10.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A Comparison of Affine Region Detectors. Int. Journal of Computer Vision (accepted)Google Scholar
  11. 11.
    Schaffalitzky, F., Zisserman, A.: Automated location matching in movies. Computer Vision and Image Understanding 92(2-3), 236–264 (2003)CrossRefGoogle Scholar
  12. 12.
    Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. Int. J. of Robotics Research 21(8), 735–758 (2002)CrossRefGoogle Scholar
  13. 13.
    UCH100 database. Electronically available in: http://vision.die.uchile.cl/

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Patricio Loncomilla
    • 1
  • Javier Ruiz-del-Solar
    • 1
  1. 1.Department of Electrical EngineeringUniversidad de ChileChile

Personalised recommendations