Unification of Multichannel Motion Feature Using Boolean Polynomial

  • Naoya Ohnishi
  • Atsushi Imiya
  • Tomoya Sakai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5876)


We develop an algorithm to unify features extracted from a multichannel image using the Boolean function. The colour optical flow enables to detect illumination-robust motion features in the environment. Our algorithm robustly detects free-space for robot navigation from a colour video sequence. We experimentally show that colour-optical-flow-based free-space detection is stable against illumination change in an image sequence.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Angulo, J., Serra, J.: Color segmentation by ordered mergings. In: Proc. ICIP 2003, vol. 2, pp. 125–128 (2003)Google Scholar
  2. 2.
    Batavia, P.H., Singh, S.: Obstacle detection using adaptive color segmentation and color stereo homography. In: ICRA 2001, pp. 705–710 (2001)Google Scholar
  3. 3.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)CrossRefGoogle Scholar
  4. 4.
    Bouguet, J.-Y.: Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm, Intel Corporation, Microprocessor Research Labs, OpenCV Documents (1999)Google Scholar
  5. 5.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)Google Scholar
  6. 6.
    Golland, P., Bruckstein, A.M.: Motion from color CVIU, vol. 68, pp. 346–362 (1997)Google Scholar
  7. 7.
    Andrews, R.J., Lovell, B.C.: Color optical flow. In: Proc. Workshop on Digital Image Computing, pp. 135–139 (2003)Google Scholar
  8. 8.
    van de Weijer, J., Gevers, Th.: Robust optical flow from photometric invariants. In: Proc. ICIP, pp. 1835–1838 (2004)Google Scholar
  9. 9.
    Barron, J.L., Klette, R.: Quantitative color optical flow. In: Proceedings of 16th ICPR, vol. 4, pp. 251–255 (2002)Google Scholar
  10. 10.
    Heigl, B., Paulus, D., Niemann, H.: Tracking points in sequences of color images. In: Proceedings 5th German-Russian Workshop on Pattern Analysis, pp. 70–77 (1998)Google Scholar
  11. 11.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Ohnishi, N., Imiya, A.: Featureless robot navigation using optical flow. Connection Science 17, 23–46 (2005)CrossRefGoogle Scholar
  13. 13.
    Mileva, Y., Bruhn, A., Weickert, J.: Illumination-robust variational optical flow with photometric invariants. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 152–162. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Naoya Ohnishi
    • 1
  • Atsushi Imiya
    • 2
  • Tomoya Sakai
    • 2
  1. 1.School of Science and TechnologyChiba University 
  2. 2.Institute of Media and Information TechnologyChiba UniversityChibaJapan

Personalised recommendations