Advertisement

Fast Color-Based Object Recognition Independent of Position and Orientation

  • Martijn van de Giessen
  • Jürgen Schmidhuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

Small mobile robots typically have little on-board processing power for time-consuming vision algorithms. Here we show how they can quickly extract very dense yet highly useful information from color images. A single pass through all pixels of an image serves to segment it into color-dependent regions and to compactly represent it by a short list of the average hues, saturations and color intensities of its regions; all other information is discarded. Experiments with two image databases show that in 90 % of all cases the remaining information is sufficient for a simple weighted voting algorithm to recognize objects shown in query images, independently of position and orientation and partial occlusions.

Keywords

Mobile Robot Recognition Rate Recurrent Neural Network Color Information Image Code 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Williams, R.J., Zipser, D.: Gradient-based learning algorithms for recurrent networks and their computational complexity. In: Back-propagation: Theory, Architectures and Applications. Erlbaum, Hillsdale (1994)Google Scholar
  2. 2.
    Pearlmutter, B.A.: Gradient calculations for dynamic recurrent neural networks: A survey. IEEE Transactions on Neural Networks 6, 1212–1228 (1995)CrossRefGoogle Scholar
  3. 3.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9, 1735–1780 (1997)CrossRefGoogle Scholar
  4. 4.
    Tuytelaars, T., Van Gool, L.: Wide baseline stereo matching based on local, affinely invariant regions. In: British Machine Vision Conference (2000)Google Scholar
  5. 5.
    Nene, S.A., Nayar, S.K.: A simple algorithm for nearest neighbor search in high dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997)Google Scholar
  6. 6.
    Smith, J.R., Chang, S.-F.: Visualseek: A fully automated content-based image query system. In: ACM Multimedia, pp. 87–98 (1996)Google Scholar
  7. 7.
    Shao, H., Svoboda, T., Van Gool, L.: ZuBuD — Zürich buildings database for image based recognition. Technical Report 260, Computer Vision Laboratory, Swiss Federal Institute of Technology (2003), Database downloadable from http://www.vision.ee.ethz.ch/showroom/
  8. 8.
    Nene, S., Nayar, S., Murase, H.: Columbia object image library: Coil-100. Technical Report CUCS-006-96, Department of Computer Science, Columbia University (1996)Google Scholar
  9. 9.
    Shao, H., Svoboda, T., Tuytelaars, T., Van Gool, L.: Hpat indexing for fast object/scene recognition based on local appearance. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 71–80. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Martijn van de Giessen
    • 1
  • Jürgen Schmidhuber
    • 2
  1. 1.IDSIAMannoSwitzerland
  2. 2.TU MunichGarching, MünchenGermany

Personalised recommendations