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Camera-Based Signage Detection and Recognition for Blind Persons

  • Shuihua Wang
  • Yingli Tian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7383)

Abstract

Signage plays an important role for wayfinding and navigation to assist blind people accessing unfamiliar environments. In this paper, we present a novel camera-based approach to automatically detect and recognize restroom signage from surrounding environments. Our method first extracts the attended areas which may content signage based on shape detection. Then, Scale-Invariant Feature Transform (SIFT) is applied to extract local features in the detected attended areas. Finally, signage is detected and recognized as the regions with the SIFT matching scores larger than a threshold. The proposed method can handle multiple signage detection. Experimental results on our collected restroom signage dataset demonstrate the effectiveness and efficiency of our proposed method.

Keywords

Blind people Navigation and wayfinding Signage detection and recognition 

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References

  1. 1.
    Everingham, M., Thomas, B., Troscianko, T.: Wearable Mobility Aid for Low Vision Using Scene Classification in a Markov Random Field Model Framework. International Journal of Human Computer Interaction 15, 231–244 (2003)CrossRefGoogle Scholar
  2. 2.
    Hasanuzzaman, F., Yang, X., Tian, T.: Robust and Effective Component-based Banknote Recognition for the Blind. IEEE Transactions on Systems, Man, and Cybernetics–Part C: Applications and Reviews 41(5) (2011), 10.1109/TSMCC.2011.2178120Google Scholar
  3. 3.
    Seeing with Sound – The voice, http://www.seeingwithsound.com/
  4. 4.
    Ivanchenko, V., Coughlan, J., Shen, H.: Crosswatch: A Camera Phone System for Orienting Visually Impaired Pedestrians at Traffic Intersections. In: Miesenberger, K., Klaus, J., Zagler, W.L., Karshmer, A.I. (eds.) ICCHP 2008. LNCS, vol. 5105, pp. 1122–1128. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Kocur, I., Parajasegaram, R., Pokharel, G.: Global Data on Visual Impairment in the Year 2002. Bulletin of the World Health Organization 82 (2004)Google Scholar
  6. 6.
    Lindeberg, T.: Scale-space theory: A basic tool for analyzing structures at different scales. J. Appl. Statist. 21, 224–270 (2004)Google Scholar
  7. 7.
    Mikolajczyk, K., Schmid, C.: An Affine Invariant Interest Point Detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)CrossRefGoogle 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.
    Matsui, Y., Miyoshi, Y.: Difference-of-Gaussian-Like Characteristics for Optoelectronic Visual Sensor. Signal Processing & Analysis 7, 1447–1452 (2007)Google Scholar
  10. 10.
    Omachi, M., Omachi, S.: Traffic light detection with color and edge information. In: 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, pp. 284–287 (2009)Google Scholar
  11. 11.
    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
  12. 12.
    Shen, H., Coughlan, J.: Grouping Using Factor Graphs: An Approach for Finding Text with a Camera Phone. In: Escolano, F., Vento, M. (eds.) GbRPR. LNCS, vol. 4538, pp. 394–403. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Shoval, S., Ulrich, I., Borenstein, J.: Computerized Obstacle Avoidance Systems for the Blind and Visually Impaired. In: Teodorescu, H.N.L., Jain, L.C. (eds.) Invited chapter in Intelligent Systems and Technologies in Rehabilitation Engineering, pp. 414–448. CRC Press (2000)Google Scholar
  14. 14.
    The Smith-Kettlewell Rehabilitation Engineering Research Center (RERC) develops new technology and methods for understanding, assessment and rehabilitation of blindness and visual impairment, http://www.ski.org/Rehab/
  15. 15.
    Wang, S.H., Tian, Y.L.: Indoor signage detection based on saliency map and Bipartite Graph matching. In: International Workshop on Biomedical and Health Informatics (2011)Google Scholar
  16. 16.
    Yang, X., Yuan, S., Tian, Y.: Recognizing Clothes Patterns for Blind People by Confidence Margin based Feature Combination. In: International Conference on ACM Multimedia (2011)Google Scholar
  17. 17.
    Yang, X., Tian, Y., Yi, C., Arditi, A.: Context-based Indoor Object Detection as an Aid to Blind Persons Accessing Unfamiliar Environment. In: International Conference on ACM Multimedia (2010)Google Scholar
  18. 18.
    Yi, C., Tian, Y.: Text Detection in Natural Scene Images by Stroke Gabor Words. In: The 11th International Conference on Document Analysis and Recognition, ICDAR (2011)Google Scholar
  19. 19.
    Yi, C., Tian, Y.: Text String Detection from Natural Scenes by Structure-based Partition and Grouping. IEEE Transactions on Image Processing 20(9) (2011), PMID: 21411405Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shuihua Wang
    • 1
  • Yingli Tian
    • 1
  1. 1.Department of Electrical EngineeringThe City College, City University of New YorkNew YorkUSA

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