A Segmentation-Based Stereovision Approach for Assisting Visually Impaired People

  • Hao Tang
  • Zhigang Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7383)


An accurate 3D map, automatically generated in real-time from a camera-based stereovision system, is able to assist blind or visually impaired people to obtain correct perception and recognition of the surrounding objects and environment so that they can move safely. In this paper, a segmentation-based stereovision approach is proposed to rapidly obtain accurate 3D estimations of man-made scenes, both indoor and outdoor, with largely textureless areas and sharp depth changes. The new approach takes advantage of the fact that many man-made objects in an urban environment consist of planar surfaces. The final outcome of the system is not just an array of individual 3D points. Instead, the 3D model is built in a geometric representation of plane parameters, with geometric relations among different planar surfaces. Based on this 3D model, algorithms can be developed for traversable path planning, obstacle detection and object recognition for assisting the blind in urban navigation.


Interest Point Plane Parameter Stereo Match Epipolar Line Impaired People 
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.
    Aguerrevere, D., Choudhury, M., Barreto, A.: Portable 3D sound / sonar navigation system for blind individuals. In: The 2nd LACCEI Int. Latin Amer. Caribbean Conf. Eng. Technol. Miami, FL, June 2–4 (2004)Google Scholar
  2. 2.
    Audette, R., Balthazaar, J., Dunk, C., Zelek, J.: A stereo-vision system for the visually impaired, Sch. Eng., Univ. Guelph, Guelph, ON, Canada, Tech. Rep. 2000-41x-1 (2000)Google Scholar
  3. 3.
    Bouzit, M., Chaibi, A., De Laurentis, K.J., Mavroidis, C.: Tactilefeedback navigation handle for the visually impaired. In: ASME Int. Mech. Eng. Congr. RD&D Expo., Anaheim, CA, November 13–19 (2004)Google Scholar
  4. 4.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Patten Analysis and Machine Intelligence 23(11) (November 2001)Google Scholar
  5. 5.
    Cardin, S., Thalmann, D., Vexo, F.: A wearable system for mobility improvement of visually impaired people. Vis. Comput. 23(2), 109–118 (2007)CrossRefGoogle Scholar
  6. 6.
    Comanicu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Patten Analysis and Machine Intelligence (May 2002)Google Scholar
  7. 7.
    Coughlan, J., Manduchi, R., Shen, H.: Cell phone-based wayfinding for the visually impaired. In: 1st International Workshop on Mobile Vision (2006)Google Scholar
  8. 8.
    Manduchi, R., Coughlan, J., Ivanchenko, V.: Search Strategies of Visually Impaired Persons Using a Camera Phone Wayfinding System. In: Miesenberger, K., Klaus, J., Zagler, W.L., Karshmer, A.I. (eds.) ICCHP 2008. LNCS, vol. 5105, pp. 1135–1140. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Dakopoulos, D., Bourbakis, N.: Wearable obstacle avoidance electronic travel aids for blind: a survey. IEEE Trans. on Systems, Man, and Cybernetics, January 1 (2010)Google Scholar
  10. 10.
    Gonzalez-Mora, J.L., Rodrıguez-Hernandez, A., Rodrıguez-Ramos, L.F., Dıaz-Saco, L., Sosa, N.: Development of a new spaceperception system for blind people, based on the creation of a virtual acousticspace. Tech. Rep., May 8 (2009)Google Scholar
  11. 11.
    Hub, A., Diepstraten, J., Ertl, T.: Design and development of an indoor navigation and object identification system for the blind. In: Proc. ACMSIGACCESS Accessibility Computing, vol. 77–78, pp. 147–152 (September 2003/January 2004)Google Scholar
  12. 12.
    Ifukube, T., Sasaki, T., Peng, C.: A blind mobility aid modeled after echolocation of bats. IEEE Trans. Biomed. Eng. 38(5), 461–465 (1991)CrossRefGoogle Scholar
  13. 13.
    Liu, J., Cong, Y., Li, X., Tang, Y.: A stairway detection algorithm based on vision for UGV stair climbing. In: IEEE Networking, Sensing and Control (2008)Google Scholar
  14. 14.
    Lu, X., Manduchi, R.: Detection and localization of curbs and stairways using stereo vision. In: IEEE International Conference on Robotics and Automation, ICRA (2005)Google Scholar
  15. 15.
    Meijer, P.B.L.: An experimental system for auditory image representations. IEEE Trans. Biomed. Eng. 39(2), 112–121 (1992)CrossRefGoogle Scholar
  16. 16.
    Pradeep, V., Medioni, G., Weiland, J.: Piecewise planar modeling for step detection using stereo vision. In: Workshop on Computer Vision Applications for the Visually Impaired (2008)Google Scholar
  17. 17.
    Se, S., Michael, B.: Vision-based Detection of Stair-cases. In: Asian Conference on Computer Vision, ACCV (2000)Google Scholar
  18. 18.
    Se, S.: Zebra-crossing detection for the partially sighted. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR (2000)Google Scholar
  19. 19.
    Shah, C., Bouzit, M., Youssef, M., Vasquez, L.: Evaluation of RU-netra–tactile feedback navigation system for the visually impaired. In: Proc. Int. Workshop Virtual Rehabil, New York, pp. 71–77 (2006)Google Scholar
  20. 20.
    Shoval, S., Borenstein, J., Koren, Y.: Mobile robot obstacle avoidance in a computerized travel aid for the blind. In: Proc, IEEE Int. Conf. Robot. Autom., San Diego, CA, May 8–13, pp. 2023–2029 (1994)Google Scholar
  21. 21.
    Tao, H., Sawhney, H.S., Kumar, R.: A global matching framework for stereo computation. In: Proc. Int. Conf. Computer Vision (2001)Google Scholar
  22. 22.
    Second Sight, (last visited April 2012)
  23. 23.
    BrainPort Vision Technology, (last visited April 2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hao Tang
    • 1
  • Zhigang Zhu
    • 1
  1. 1.Department of Computer ScienceCUNY City CollegeNew YorkUSA

Personalised recommendations