Advertisement

International Journal of Computer Vision

, Volume 24, Issue 1, pp 79–94 | Cite as

Dynamic Vergence Using Log-Polar Images

  • C. Capurro
  • F. Panerai
  • G. Sandini
Article

Abstract

Vergence provides robot vision systems with a crucial degree of freedom: it enables fixation of points in visual space at different distances from the observer. Vergence control, therefore, affects the performance of the stereo system as well as the results of motion estimation and tracking and, as such, must satisfy different requirements in order to be able to provide not only a stable fixation, but a stable binocular fusion, and a fast, smooth and accurate reaction to changes in the environment. To obtain this kind of performance the paper focuses specifically on the use of dynamic visual information to drive vergence control. In this context, moreover, the use of a space-variant, anthropomorphic sensor is described and some advantages in relation to vergence control are discussed to demonstrate the relevance of image plane geometry for this particular task. Expansion or contraction patterns and the temporal evolution of the degree of fusion measured in the log-polar domain are the inputs to the vergence control system and determine robust and accurate steering of the two cameras. Real-time experiments are presented to demonstrate the performance of the system covering different key situations.

log-polar active vision vergence fusion divergence 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allman, J. and Kaas, J. 1971. Representation of the visual field in striate and adjoining cortex of the owl monkey (aotus trivirgatus). Brain Res., 35: 89-106.Google Scholar
  2. Bernardino, A. and Santos-Victor, J. 1996. Vergence control for robotics heads using log-polar images. In Proc. Int. Conference on Intelligent Robots and Systems, Osaka-Japan. JRS-IEEE.Google Scholar
  3. Capurro, C., Panerai, F., Sandini, G. 1995. Space variant vision for an active camera mount. In Proc. SPIE AeroSense95, Orlando, Florida.Google Scholar
  4. Capurro, C., Panerai, F., and Sandini, G. 1996. Vergence and tracking fusing log-polar images. In Proc. Int. Conference on Pattern Recognition, Vienna-Austria.Google Scholar
  5. Carpenter, R. 1991. Eye Movements. The Macmillan Press.Google Scholar
  6. Cipolla, R. and Blake, A. 1992. Surface orientation and time to contact from image divergence and deformation. In Proc. ECCV-92, G. Sandini (Ed.), S. Margherita Ligure-Italy. Springer Verlag.Google Scholar
  7. Coombs, D. and Brown, C. 1990. Intelligent gaze control in binocular vision. In Proc. of the Fifth IEEE International Symposium on Intelligent Control, Philadelphia, PA.Google Scholar
  8. Coombs, D., Olson, T., and Brown, C. 1990. Gaze control and segmentation. In Proc. of the AAAI-90 Workshop on Qualitative Vision, Boston, MA.Google Scholar
  9. Coombs, D. and Brown, C. 1993. Real-time binocular smooth pursuit. Int. Journal of Computer Vision, 11(2): 147-164.Google Scholar
  10. Cowey, A. 1964. Projection of the retina on to striate and prestriate cortex in the squirrel monkey, saimiri sciureus. J. Neurophysiol., 27: 266-293.Google Scholar
  11. Daniel, M. and Whitteridge, D. 1961. The representation of the visual field on the cerebral cortex in monkeys. J. Physiol. (London), 159: 203-221.Google Scholar
  12. Debusschere, I., Bronckaers, E., Claeys, C., Kreider, G., der Spiegel, J. V., Bellutti, P., Soncini, G., Dario, P., Fantini, F., and Sandini, G. 1989. A 2d retinal ccd sensor for fast 2d shape recognition and tracking. In Proc. 5th Int. Solid-State Sensor and Transducers, Montreux.Google Scholar
  13. Ferrari, F., Sandini, G., Hermans, L., Guerin, C., Manganas, A., Dario, P., and Frowein, H. 1994. Tide project 1038 ibidem, technical annex. Technical Report, ibidem Consortium.Google Scholar
  14. Ferrari, F., Nielsen, P. Q. J., and Sandini, G. 1995. Space variant imaging. Sensor Review, 15(2): 17-20.Google Scholar
  15. Fisher, T. and Juday, R. 1988. A programmable video image remapper. In Proc. SPIE, 938: 122-128.Google Scholar
  16. Griswold, N. C., Lee, J., and Weiman, C. 1992. Binocular fusion revisited utilizing a log-polar tessellation. Computer Vision and Image Processing, pp. 421-457.Google Scholar
  17. Horn, B. K. P. 1986. Robot Vision. MIT Press: Cambridge, USA.Google Scholar
  18. Hubel, D. and Wiesel, T. 1977. Functional architecture of macaque monkey cortex. Proc. R. Soc. Lond., 198: 1-59.Google Scholar
  19. Irani, M., Rousso, B., and Peleg, S. 1994. Recovery of egomotion using image stabilization. In Proc. IEEE CVPR, Seattle, USA.Google Scholar
  20. Jenkin, M. and Tsotsos, J. K. 1991. Techniques for disparity measurement. CVGIP: Image Understanding, 53(1).Google Scholar
  21. Judge, S. J. (1991). Vergence. In Eye Movements, R. Carpenter (Ed.), CRC Press, (7): 157-172.Google Scholar
  22. Julesz, B. 1986. Stereoscopic vision. Vision Res., 26(9): 1601-1602.Google Scholar
  23. Koenderink, J. and van Doorn, J. 1991. Affine structure from motion. Journal of the Optical Society of America, 8(2): 377-385.Google Scholar
  24. Negahdaripour, S. and Lee, S. 1992. Motion recovery from images sequences using only first order optical flow information. IJCV, 9(3): 163-184.Google Scholar
  25. Nelson, R. and Aloimonos, J. 1989. Obstacle avoidance using flow field divergence. IEEE Trans. on PAMI, PAMI-11(10): 1102- 1106.Google Scholar
  26. Nielsen, J. and Sandini, G. 1994. Learning mobile robot navigation: A behavior-based approach. In IEEE Int. Conf. on Systems, Man and Cybernetics, San Antonio, Texas.Google Scholar
  27. Nordlund, T. U. P. 1995. Closing the loop: Pursuing a moving object by a moving observer. In Proc. 6th Int. Conf. on Computer Analysis of images and Patterns.Google Scholar
  28. Ogle, K. 1964. Researches in Binocular Vision. Hafner Publishing Company: London.Google Scholar
  29. Pahlavan, K., Uhlin, T., and Eklund, J. 1992. Integrating primary ocular processes. In Proc. Second European Conference on Computer Vision, Santa Margherita, Italy. Springer-Verlag, pp. 526-541.Google Scholar
  30. Questa, P. and Sandini, G. 1996. Time to contact computation with a space-variant retina-like c-mos sensor. In Proc. Int. Conference on Intelligent Robots and Systems, Osaka, Japan. JRS-IEEE.Google Scholar
  31. Rojer, A. and Schwartz, E. 1990. Design considerations for a space variant visual sensor with complex logarithmic geometry. In Proc. Int. Conf. on Pattern Recognition, Philadelphia, PA.Google Scholar
  32. Sanger, T. D. 1988. Stereo disparity computation using gabor filter. Biological Cybernetics, 59: 405-418.Google Scholar
  33. Santos-Victor, J. and Sandini, G. 1995. Visual based obstacle detection: A purposive approach using normal flow. In Proc. of the Int. Conf. on Intelligent Autonomous Systems, Karlsruhe, Germany.Google Scholar
  34. Scheffer, D., Dierickx, B., and Pardo, F. 1996. Log-polar image sensor in cmos technology. In Proc. Europto, Besancon.Google Scholar
  35. Schwartz, E. L. 1977. Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception. Biol. Cybernetics, 25: 181-194.Google Scholar
  36. Schwartz, E. L. 1980. A quantitative model of the functional architecture of human striate cortex with application to visual illusion and cortical texture analysis. Biological Cybernetics, 37: 63-76.Google Scholar
  37. Sharkey, P., Murray, D., Vandevelde, S., Reid, I., and McLauchlan, P. 1993. A modular head/eye platform for real-time reactive vision. Mechatronics, 3(4).Google Scholar
  38. Subbarao, M. and Waxman, A. 1986. Closed from solutions to image flow equations for planar surfaces in motion. CVGIP, 36: 208- 228.Google Scholar
  39. Sundereswaran, V. 1991. Egomotion from global flow field data. In Proc. of the IEEE Workshop on Visual Motion, Princeton, NJ- USA.Google Scholar
  40. Theimer, W. M., Mallot, H. A., and Tolg, S. 1992. Phase method for binocular vergence control and depht reconstruction. In Proc. SPIE Intelligent Robots and Computer Vision XI, Boston, Massachussetts.Google Scholar
  41. Tistarelli, M. and Sandini, G. 1993. On the advantages of polar and log-polar mapping for direct estimation of time-to-impact from optical flow. IEEE Transactions on PAMI, 14(4): 401-410.Google Scholar
  42. Tunley, H. and Young, D. (1994). First order optical flow from logpolar sampled images. In Proc. Third European Conference on Computer Vision.Google Scholar
  43. Vleeschauwer, D. D. 1993. An intensity-based coarse-to-fine approach to reliably measure binocular disparity. CVGIP: Image Understanding, 57(2).Google Scholar
  44. Weiman, C. 1995. Binocular stereo via log-polar retinas. In Proc. SPIE AeroSense95, Orlando, Florida.Google Scholar
  45. Weiman, C. F. R. and Juday, R. D. 1990. Tracking algorithms using log-polar mapped image coordinates. In SPIE Int. Conf. on Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, Philadelphia (PA), 1192: 843-853.Google Scholar
  46. Weiman, C. and Fisher, T. 1994. Log-polar binocular vision system. Technical Report NAS 9-18637, Nasa Final Report, Danbury, CT.Google Scholar

Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • C. Capurro
    • 1
  • F. Panerai
    • 1
  • G. Sandini
    • 1
  1. 1.LIRA Lab, Department of Communication Computer and Systems ScienceUniversity of GenovaItaly

Personalised recommendations