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A Computational Model for Landmarks Acquisition in Positioning

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Abstract

This paper presents a computational model for landmarks acquisition in vehicle’s positioning. Considering the machine vision, visual attention mechanism of biology, feature-integration theory and the demand of navigation, the computational model is divided into two part: pre-attention state and attention state, and the detailed process includes extraction of feature points, generation of local saliency value, generation of integration saliency value and selection of attention points. At the end of the paper, the proposed computational model is realized through the simulation. What’s more, the robustness of the attention points and the vehicle’s positioning performance when the attention points are used as landmarks are analyzed. Simulation validates that the computational model is availability. Besides, in terms of the performance of the self-attribute and providing positioning reference, the attention points behave well.

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References

  1. Wu, D.W., Tai, N.J, Qi, J.Y.: A new research progress of UCAV intelligent navigation based on cognitive theory. J. Air Force Eng. Univ.: Nat. Sci. Ed. 12(4), 52–57 (2011)

    Google Scholar 

  2. Tai, N.J., Wu, D.W., Qi, J.Y.: A method to extract high robust keypoints based on improved SIFT. Acta Aeronaut. et Astronaut. Sin. 33(2), 2313–2321 (2012)

    Google Scholar 

  3. Ouerhani, N., Hugli, H., Gruener, G., Codoure, A.: A visual attention-based approach for automatic landmark selection and recognition. Lect. Notes Comput. Sci. 3368, 183–195 (2005)

    Article  Google Scholar 

  4. V’azquez-Mart’ın, R., del Toro, J.C., Bandera, A., Sandoval, F.: Data-and model-driven attention mechanism for autonomous visual landmark acquisition. In: IEEE International Conference on Robotics and Automation, pp 3372–3377 (2005)

  5. Kadir, H.A., Arshad. M.R.: Features detection and matching for visual simultaneous Localization and mapping (vslam). In: IEEE International Conference on Control System, Computing and Engineering, pp 40–44 (2013)

  6. Nguyen, Q.K., Le, T.L., Pham, N.H.: Leaf based plant identification system for Android using SURF features in combination with Bag of Words model and supervised learning. In: International Conference on Advanced Technologies for Communications, pp 404–407 (2013)

  7. Gui, Y., Su, A., Du, J.: Point-pattern matching method using SURF and Shape Context. Optik 124, 1869–1873 (2013)

    Article  Google Scholar 

  8. Wu, T.F., Gao, J., Zhao, Q.: A computational model of object-based selective visual attention mechanism in visual information acquisition. In: International Conference on Information Acquisition, pp 405–409 (2004)

  9. Yin, Y., Ding, J., Lai, J.L.: A novel video salient object extraction method based on visual attention. Signal Process.: Image Commun. 48, 45–54 (2013)

    Google Scholar 

  10. Shao, X.G., Gao, K., Lv, L.: A new efficient method for color image compression based on visual attention mechanism. In: Proceedings of SPIE, pp. 78501G, 1-8 (2010)

  11. Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194–230 (2001)

    Article  Google Scholar 

  12. Navalpakkam, V., Itti, L.: An integrated model of top-down and bottom-up attention for optimizing detection speed. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 2049–2056 (2006)

  13. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 17–22 (2007)

  14. Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion Fourer transform. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1–8 (2008)

  15. Kanwisher, N., Wojciulik. E.: Visual attention: insights from brain imaging. Nat. Neurosci. 1, 91–100 (2000)

    Article  Google Scholar 

  16. Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12, 97–136 (1980)

    Article  Google Scholar 

  17. Harris, C., Stephens, M.: Combined corner and edge detector. In: Proceedings of the fourth Alvey Vision Conference, pp 147–151. University Manchester (1988)

  18. Lowe, D.G.: Object recognition from local scale- invariant feature. In: IEEE International Conference of Computer Vision, pp 1150–1157 (1999)

  19. Bay, H., Tuvtellars, T., Gool, L.V.: SURF: speeded up robust feature. In: Proceedings of the ninth European Conference on Computer Vision, pp 404–417 (2006)

  20. Saraf, M., Mohammadi, K., Mosavi, M.R.: Classifying the geometric dilution of precision of GPS satellites utilizing Bayesian decision theory. Comput. Electr. Eng. 37, 1009–1018 (2011)

    Article  Google Scholar 

  21. Azami, H., Mosavi, M.R., Sanei, S.: Classification of GPS satellites using improved back propagation training algorithms. Wirel. Pers. Commun. 71, 789–803 (2013)

    Article  Google Scholar 

  22. Wu, C.H., Ho, Y.W., Chen, L.W., Huang, Y.D.: Discovering approximate expressions of GPS geometric dilution

  23. Caffery, J.: Wireless location in CDMA cellular radio systems. Kluwer Academic Publisheers, Boston (1999)

    Google Scholar 

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Authors and Affiliations

  1. College of Information and Navigation, Air Force engineering University, Xi’an, Shanxi, 710077, China

    Yang Zhou, Dewei Wu, Jia Du & Weilong Li

  2. Xi’an Communications Institute, Xi’an, Shanxi, 710065, China

    Jia Du

Authors
  1. Yang Zhou
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  2. Dewei Wu
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  3. Jia Du
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  4. Weilong Li
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Correspondence to Yang Zhou.

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Zhou, Y., Wu, D., Du, J. et al. A Computational Model for Landmarks Acquisition in Positioning. J Intell Robot Syst 82, 537–553 (2016). https://doi.org/10.1007/s10846-015-0276-1

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  • DOI: https://doi.org/10.1007/s10846-015-0276-1

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