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MCPN, Octree Neighbor Finding During Tree Model Construction Using Parental Neighboring Rule

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3D Research

Abstract

A new method for octree neighbor finding is proposed, named during Model Construction using Parental Neighboring rule (MCPN). Our proposed method finds and stores all neighbors of all leaf nodes during tree model construction, unlike majority of previous neighbor finding methods in which constructed tree model is the input of neighbor finding algorithm. Considering Parental Neighboring Rule (PNR), neighbor finding during tree model construction causes no increase in its time complexity class. The proposed method finds all neighbors of a node in 26 possible directions regardless of their size. Experimental results show that both in quadtrees and octrees, MCPN’s needed time to find and store all neighbors of all leaf nodes plus tree model construction time, relative to tree model construction time alone, converges to one as the level of tree increases. Also MCPN outclasses the latest constant time neighbor finding method for quadtrees proposed by Aizawa and Tanaka.

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References

  1. Payeur, P. (2004). An optimized computational technique for free space localization in 3-D virtual representations of complex environments. In Proceedings of the 2004 IEEE Virtual Environment, Human-Computer Interfaces, and Measurement Systems, VECIMS, 2004. Boston.

  2. Samet, H. (1989). Neighbor finding in images represented by octrees. Computer Vision, Graphics, and Image Processing, 46(3), 367–386.

    Article  Google Scholar 

  3. Kim, J., & Lee, S. (2009). Fast neighbor cells finding method for multiple octree representation. In 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) (540–545). Daejeon.

  4. Goerzen, C., Kong, Z., & Mettler, B. (2010). A survey of motion planning algorithms from the perspective of autonomous UAV guidance. Journal of Intelligent Robotic Systems, 57, 65–100.

    Article  MATH  Google Scholar 

  5. Schrack, G. (1992). Finding neighbors of equal size in linear quadtrees and octrees in constant time. Computer Vision, Graphics, and Image Processing: Image Understanding, 55(3), 231–239.

    Google Scholar 

  6. Gargantini, I. (1982). Linear oct-trees for fast processing of three-dimensional objects. Computer Graphics and Image Processing, 20, 365–374.

    Article  Google Scholar 

  7. Voros, J. (2000). A strategy for repetitive neighbor finding in octree representations. Image and Vision Computing, 18, 1085–1091.

    Article  Google Scholar 

  8. Klinger, A., & Rhodes, M. (1979). Organization and access of image data by areas. In IEEE Pallem Analysis and Machine Intelligence I (pp. 50–60).

  9. Payeur, P. (2006). A computational technique for free space localization in 3-D multi-resolution probabilistic environment models. Instrumentation and Measurement, 50(5), 1734–1746.

    Article  Google Scholar 

  10. Yoder, R., Bloniarz, P. (2006). A practical algorithm for computing neighbors in quadtrees, octrees, and hyperoctrees. In International Conference on Modeling, Simulation and Visualization (249–255), Las Vegas: CSREA Press.

  11. Aizawa, K., & Tanaka, S. (2009). A constant-time algorithm for finding neighbors in quadtrees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(7), 1178–1183.

    Article  Google Scholar 

  12. Aizawa, K., Motomura, K., Kimura, S., Kadowaki, R., & Fan J. (2008). Constant time neighbor finding in quadtrees: An experimental result. In ISCCSP 2008. Malta.

  13. Hunter, G., & Steiglitz, K. (1979). Operations on images using quad trees. In IEEE Transactions on Pallem Analysis and Machine Intelligence I, (pp. 145–163).

  14. Karimi, J., & Pourtakdoust, S. H. (2013). Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm. Aerospace Science and Technology, 26, 60–71.

    Article  Google Scholar 

  15. Jun, M., D’Andrea, R. (2003). Path planning for unmanned aerial vehicles in uncertain and adversarial environments. In Cooperative control: models, applications and algorithms (pp. 95–110). New york: Springer.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran

    Mohammad Hasan Namdari & Seyed Reza Hejazi

  2. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran

    Maziar Palhang

Authors
  1. Mohammad Hasan Namdari
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  2. Seyed Reza Hejazi
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  3. Maziar Palhang
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Correspondence to Mohammad Hasan Namdari.

Appendix

Appendix

See Figs. 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, and 23

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Namdari, M.H., Hejazi, S.R. & Palhang, M. MCPN, Octree Neighbor Finding During Tree Model Construction Using Parental Neighboring Rule. 3D Res 6, 29 (2015). https://doi.org/10.1007/s13319-015-0060-9

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  • DOI: https://doi.org/10.1007/s13319-015-0060-9

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