International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Detecting Doors Edges in Diverse Environments for Visually Disabled People

  • Habib, Mohamed Ibrahim (Computer Science Department, College of Computing and Informatics, Saudi Electronic University, Faculty of Engineering, Port Said University)
  • 투고 : 2021.05.05
  • 발행 : 2021.05.30
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초록

It is a challenge for visually impaired people to access unfamiliar environments independently, hence the quality of life is reduced, and safety of life is compromised. An accurate and reliable door detection system comprising of way finding and indoor navigation can be beneficial for a large number of autonomous and mobile applications for visually impaired people. This paper illustrates an image-based door detection scheme for visually impaired people using stable features (edges and corners) including color averaging and image resizing. Simulation results show that the proposed scheme shows a significant improvement when compared with existing scheme.

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참고문헌

  1. World Health Organization, "Global data on visual impairment." [Online]. Available: https://www.who.int/blindness/publications/globaldata/en/. [Accessed: 20-Mar-2021].
  2. X. Yang, and Y. Tian, "Robust door detection in unfamiliar environments by combining edge and corner features," IEEE Computer Vision and Pattern Recognition Workshops, pp. 57-64, 2010.
  3. X. Yang, Y. Tian,, C. Yi, and A. Arditi, "Context-based indoor object detection as an aid to blind persons accessing unfamiliar environments," International conference on Multimedia, pp. 1087-1090, 2010
  4. R. Sekkal, F. Pasteau, M. Babel, B. Brun, and I. Leplumey, "Simple monocular door detection and tracking," IEEE International Conference on Image Processing, pp. 3929-3933, 2013.
  5. F. Mahmood, and F. Kunwar, "A Self-Organizing Neural Scheme for Door Detection in Different Environments," International Journal of Computer Applications, vol. 60, no. 9, pp. 13-18, 2013. https://doi.org/10.5120/9719-3679
  6. J. Jelinek, "Kohonen map modification for classification tasks," Proceedings of the 11th International Conference on Agents and Artificial Intelligence, 2019.
  7. J. Hensler, M. Blaich, and O. Bittel, "Real-time Door Detection Based on AdaBoost Learning Algorithm," International Conference on Research and Education in Robotics, pp. 61-73, 2009.
  8. J. Sturm, K. Konolige, C. Stachniss, and W. Burgard, "Vision-based detection for learning articulation models of cabinet doors and drawers in household environments," IEEE International Conference on Robotics and Automation, pp. 362-368, 2010.
  9. H. Zhang, L. Dou, H. Fang, and J. Chen, "Autonomous indoor exploration of mobile robots based on door-guidance and improved dynamic window approach," IEEE International Conference on Robotics and Biomimetics, pp. 408-413, 2009.
  10. C. Chen, and Y. Tian, "Door detection via signage context-based hierarchical compositional model," IEEE Computer Vision and Pattern Recognition Workshops, pp. 1-6, 2010.
  11. A. Hinkenjann, T. Roth, J. Millberg, H. Yun, and Y. Li, "Real-time simulation of camera errors and their effect on some basic robotic vision algorithms," IEEE International Conference on Computer and Robot Vision, pp. 218-225, 2013.
  12. S. Borgsen, Z.M, M. Schopfer, L. Ziegler, and S. Wachsmuth, "Automated Door Detection with a 3D-Sensor," IEEE Canadian Conference on Computer and Robot Vision, pp. 276-282, 2014.
  13. D. Dai, G. Jiang, J. Xin, X. Gao, L. Cui, Y. Ou, and G. Fu, "Detecting, locating and crossing a door for a wide indoor surveillance robot," IEEE International Conference on Robotics and Biomimetics, pp. 1740-1746, 2013.
  14. M. Vlaminck, L. Jovanov, P. V. Hese, B. Goossens, W. Philips, and A. Pizurica, "Obstacle detection for pedestrians with a visual impairment based on 3D imaging," IEEE International Conference on 3D Imaging, pp. 1-7, 2013.
  15. M. Afif, R. ayachi, Y. Said, E. Pissaloux, and M. Atri, "Recognizing signs and doors for Indoor Wayfinding for Blind and Visually Impaired Persons," 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2020.
  16. R. Trabelsi, I. Jabri, F. Melgani, F. Smach, N. Conci, and A. Bouallegue, "Indoor object recognition in RGBD images with complex-valued neural networks for visually-impaired people," Neurocomputing, vol. 330, pp. 94-103, 2019. https://doi.org/10.1016/j.neucom.2018.11.032
  17. J. Canny, "A computational approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no.6, pp. 679-698, 1986. https://doi.org/10.1109/TPAMI.1986.4767851
  18. X. C. He, and N. H. Yung, "Corner detector based on global and local curvature properties," Optical Engineering, vol. 47, no.5, pp. 057008, 2008 https://doi.org/10.1117/1.2931681