A Quantitative Comparison of Speed and Reliability for Log-Polar Mapping Techniques

  • Manuela Chessa
  • Silvio P. Sabatini
  • Fabio Solari
  • Fabio Tatti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6962)


A space-variant representation of images is of great importance for active vision systems capable of interacting with the environment. A precise processing of the visual signal is achieved in the fovea, and, at the same time, a coarse computation in the periphery provides enough information to detect new saliences on which to bring the focus of attention. In this work, different techniques to implement the blind-spot model for the log-polar mapping are quantitatively analyzed to assess the visual quality of the transformed images and to evaluate the associated computational load. The technique with the best trade-off between these two aspects is expected to show the most efficient behaviour in robotic vision systems, where the execution time and the reliability of the visual information are crucial.


Bilinear Interpolation Image Quality Assessment Image Quality Index Cortical Image Visual Information Fidelity 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aloimonos, J., Weiss, I., Bandyopadhyay, A.: Active vision. International Journal of Computer Vision 1(4), 333–356 (1988)CrossRefGoogle Scholar
  2. 2.
    Bernardino, A., Santos-Victor, J.: Visual behaviours for binocular tracking. Robotics and Autonomous Systems 25(3-4), 137–146 (1998)CrossRefGoogle Scholar
  3. 3.
    Schwartz, E.L.: Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception. Biological Cybernetics 25, 181–194 (1977)CrossRefGoogle Scholar
  4. 4.
    Zhang, X., Tay, L.P.: A spatial variant approach for vergence control in complex scenes. Image Vision Comput. 29, 64–77 (2011)CrossRefGoogle Scholar
  5. 5.
    Bernardino, A., Santos-Victor, J., Sandini, G.: Foveated active tracking with redundant 2D motion parameters. Robotics and Autonomous Systems, 205–221 (2002)Google Scholar
  6. 6.
    Bernardino, A., Santos-Victor, J.: A binocular stereo algorithm for log-polar foveated systems. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 127–136. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Yeasin, M.: Optical flow in log-mapped image plane: A new approach. IEEE Trans. Pattern Anal. Mach. Intell. 24, 125–131 (2002)CrossRefGoogle Scholar
  8. 8.
    Amiri, M., Rabiee, H.R.: A novel rotation/scale invariant template matching algorithm using weighted adaptive lifting scheme transform. Pattern Recognition 43, 2485–2496 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Smeraldi, F., Bigun, J.: Retinal vision applied to facial features detection and face authentication. Pattern Recognition Letters 23, 463–475 (2002)CrossRefzbMATHGoogle Scholar
  10. 10.
    Berton, F., Sandini, G., Metta, G.: Anthropomorphic visual sensors. In: Encyclopedia of Sensors. In: Encyclopedia of Sensors, pp. 1–16. American Scientific Publishers (2006)Google Scholar
  11. 11.
    Traver, V.J., Bernardino, A.: A review of log-polar imaging for visual perception in robotics. Robotics and Autonomous Systems 58(4), 378–398 (2010)CrossRefGoogle Scholar
  12. 12.
    Bolduc, M., Levine, M.D.: A real-time foveated sensor with overlapping receptive fields. Real-Time Imaging 3(3), 195–212 (1997)CrossRefGoogle Scholar
  13. 13.
    Jurie, F.: A new log-polar mapping for space variant imaging. Application to face detection and tracking. Pattern Recognition 32, 865–875 (1999)CrossRefGoogle Scholar
  14. 14.
    Traver, V.J., Pla, F.: Log-polar mapping template design: From task-level requirements to geometry parameters. Image Vision Comput. 26(10), 1354–1370 (2008)CrossRefGoogle Scholar
  15. 15.
    Pamplona, D., Bernardino, A.: Smooth foveal vision with Gaussian receptive fields. In: 9th IEEE-RAS Int. Conf. on Humanoid Robots, pp. 223–229 (2009)Google Scholar
  16. 16.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  17. 17.
    Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Transactions on Image Processing 15(2), 430–444 (2006)CrossRefGoogle Scholar
  18. 18.
    Young, D.S.: Straight lines and circles in the log-polar image. In: British Machine Vision Conference, pp. 426–435 (2000)Google Scholar
  19. 19.
    Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Transactions on Communications 43(12), 2959–2965 (1995)CrossRefGoogle Scholar
  20. 20.
    Sheikh, H., Wang, Z., Cormack, L., Bovik, A.: Live image quality assessment database release 2,

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Manuela Chessa
    • 1
  • Silvio P. Sabatini
    • 1
  • Fabio Solari
    • 1
  • Fabio Tatti
    • 1
  1. 1.Department of Biophysical and Electronic EngineeringUniversity of GenoaGenovaItaly

Personalised recommendations