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Enhancing Gadgets for Blinds Through Scale Invariant Feature Transform

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 823))

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Abstract

ICT can help blind people in movement and direction-finding tasks. This paper proposes a new methodology for safe mobility based on scale invariant feature transform (SIFT) that is expected to lead to higher precision and accuracy. Various existing gadgets for visually impaired are examined, and the conclusion is that the proposed methodology can enhance these gadgets.

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References

  1. Ifukube, T., Sasaki, T., Peng, C.: A blind mobility aid modeled after echolocation of bats. IEEE Trans. Biomed. Eng. 38(5), 461–465 (2002)

    Article  Google Scholar 

  2. Gerard, L., Kenneth, M., Dawson, H.: The application of robotics to a mobility aid for the elderly blind. Robot. Auton. Syst. 23(4), 245–252 (1998)

    Article  Google Scholar 

  3. Kay, L.: An Ultrasonic sensing probe as a mobility aid for the blind. Ultrasonics 2(2), 53–59 (1964)

    Article  Google Scholar 

  4. Razali, M.F., Toha, S.F., Abidin, Z.Z.: Intelligent path guidance robot for visually impaired assistance. Procedia Comput. Sci. 76, 330–335 (2015)

    Article  Google Scholar 

  5. Limburg, H., Kumar, R., Indrayan, A., et al.: Rapid assessment of prevalence of cataract blindness at district level. Int. J. Epidemiol. 26, 1049–1054 (1997)

    Article  Google Scholar 

  6. White, C.E., Bernstein, D., Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Trans. Res. C Emerg. Tech. 8, 91–108 (2000). https://doi.org/10.1016/S0968-090X(00)00026-7

    Article  Google Scholar 

  7. Lowe, David G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  8. Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, Corfu, Greece, pp. 1150–1157, September 1999

    Google Scholar 

  9. Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: ECCV, pp. 128–142 (2002); Schaffalitzky, F., Zisserman, A.: Multi-view matching for unordered image sets, or “how do i organize my holiday snaps?” In: ECCV, pp. 414–431 (2002)

    Google Scholar 

  10. Floyd, R.W.: Algorithm 97: shortest path. Commun. ACM 5(6), 345 (1962). https://doi.org/10.1145/367766.368168

    Article  Google Scholar 

  11. Warshall, S.: A theorem on Boolean matrices. J. ACM 9(1), 11–12 (1962). https://doi.org/10.1145/321105.321107

    Article  MathSciNet  MATH  Google Scholar 

  12. Prudhvi, B.R., Bagani, R.: Silicon eyes: GPS-GSM based navigation assistant for visually impaired using capacitive touch braille keypad and smart SMS facility. In: Proceedings of the 2013 World Congress on Computer and Information Technology (WCCIT), Sousse, Tunisia, 22–24 June 2013

    Google Scholar 

  13. Black, A.W., Lenzo, K.A.: Flite: a small fast run-time synthesis engine. In: Proceedings of the ITRW on Speech Synthesis; Perthshire, Scotland. 29 August–1 September 2001

    Google Scholar 

  14. Chum, O., Matas, J.: Optimal randomized. Ransac Ieee Trans. Pattern Anal. Mach. Intell. 30(8) (2008)

    Article  Google Scholar 

  15. Bharambe, S., Thakker, R., Patil, H., Bhurchandi, K.M.: Substitute eyes for blind with navigator using android. In: Proceedings of the India Educators Conference (TIIEC), Bangalore, India, 4–6 April 2013, pp. 38–43

    Google Scholar 

  16. Q. Zhang and Y. R. Chen, SIFT implementation and optimization for multi-core systems. In: Proceedings of IEEE International Symposium on Parallel and Distributed, pp. 1–8 (2008)

    Google Scholar 

  17. Selviah, D.R., Baghsiahi, H., Hindmarch, J.: 3D simulated real interior environments for mobile devices (2013)

    Google Scholar 

  18. Bonato, V., Marques, E., Constantinides, G.A.: A parallel hardware architecture for image feature detection. In: Woods, R., Compton, K., Bouganis, C., Diniz, P.C. (eds.) Reconfigurable Computing: Architectures, Tools and Applications. ARC 2008. Lecture Notes in Computer Science, vol. 4943. Springer, Berlin, Heidelberg (2008)

    Google Scholar 

  19. Bonato, V., Holanda, J.A., Marques, E.: An embedded multi-camera system for simultaneous localization and mapping. In: Bertels, K., Cardoso, J.M.P., Vassiliadis, S. (eds.) ARC 2006. LNCS, vol. 3985, pp. 109–114. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  20. Yao, L.F.: An architecture of optimised SIFT feature detection for an FPGA implementation of an image matcher. In: Proceedings of International Conference on Field-Programmable Technology, pp. 30–37 (2009)

    Google Scholar 

  21. Kim, S., Lee, H. J.: A novel hardware design for SIFT generation with reduced memory requirement. J. Semicond. Technol. Sci. 13(2), 157–169 (2013)

    Article  Google Scholar 

  22. Wang, J., Sheng, Z., Yan, L., Cao, Z.: An embedded system-on-a-chip architecture for real-time visual detection and matching. IEEE Trans. Video Technol. (2013). Accepted for publication

    Google Scholar 

  23. Zhong, S., Wang, J., Yan, L., Kang, L., Cao, Z.: A real-time embedded architecture for SIFT. J. Syst. Architect. 59(1), 16–29 (2013)

    Article  Google Scholar 

  24. Huang, Ch.: High-performance SIFT hardware accelerator for real-time image feature extraction. IEEE Trans. Circuits Syst. Video Technol. 22, 340–351 (2012)

    Article  Google Scholar 

  25. Kim, D.., Kim, K., Kim, J.Y., Lee, S. Lee, S.J., Yoo, H.J.: 81.6 GOPS object recognition processor based on a memory-centric NoC. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 17, 370–383 (2009)

    Google Scholar 

  26. Li, Z., Selviah, D.R.: Comparison of image alignment algorithms (2011)

    Google Scholar 

  27. Siggelkow, S.: Feature histograms for content-based image retrieval. Ph.D. thesis, Albert-Ludwigs-University Freiburg (2002, December)

    Google Scholar 

  28. Grabner, M., Grabner, H., Bischof, H.: Fast approximated SIFT. In: Proceedings of ACCV 2006, Hyderabad, India (2006)

    Google Scholar 

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Acknowledgements

The authors also wish to thank the anonymous reviewers for their suggestions to improve this paper.

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Correspondence to Raman Kumar .

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Kumar, R., Wiil, U.K. (2019). Enhancing Gadgets for Blinds Through Scale Invariant Feature Transform. In: Kumar, R., Wiil, U. (eds) Recent Advances in Computational Intelligence. Studies in Computational Intelligence, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-030-12500-4_9

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