[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A Fast Recovery Method of 2D Geometric Compressed Sensing Signal

  • Short Paper
  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

This paper presents a compression method based on compressed sensing for 2D contour models. And low rank random matrixes are used to sample 2D contour models, since the models can be sparsely represented under their Laplace operators. In the recovery process, a new function is designed as the optimal objective function to replace signal’s 1-norm, while a new search direction is constructed to find the solution, and to ensure that the solution speed is equal to the speed of the linear optimization. Finally, the experimental results show that the above method, boasting advanced compression ratio and good recovery effect, is well suited for processing large data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. C.L. Bajaj, V. Pascucci, G. Zhuang, Single resolution compression of arbitrary triangular meshes, in Proceedings of the Conference on Data Compression (1999), pp. 247–256

  2. E.J. Candès, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)

    Article  MATH  Google Scholar 

  3. E.J. Candès, T. Tao, Near-optimal signal recovery from random projections: universal encoding strategies. IEEE Trans. Inf. Theory 52(12), 5406–5425 (2004)

    Article  Google Scholar 

  4. M. Chow. Optimized geometry compression for real-time rendering, in Proceedings of IEEE on Visualization Conference (1997), pp. 347–354

  5. D. Cohen-Or, D. Levin, O. Remez, Progressive compression of arbitrary triangular meshes, in Proceedings of IEEE on Visualization Conference (1999), pp. 67–72

  6. M. Deering, Geometry compression, in Proceedings of ACM SIGGRAPH (1995), pp. 13–20

  7. D. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  8. Z.M. Du, G.H. Geng, 2-D geometric signal compression method based on compressed sensing, in Proceedings of IEEE on Electronics, Communications and Control (ICECC), 2011 International Conference (2011), pp. 601–604

  9. Z.M. Du, G.H. Geng, 3-D geometric signal compression method based on compressed sensing, in Proceedings of IEEE on Image Analysis and Signal Processing (IASP), 2011 International Conference (2011), pp. 62–66

  10. S. Gumhold, W. Strasser, Real time compression of triangle mesh connectivity, in Proceedings of ACM SIGGRAPH (1998), pp. 133–140

  11. M. Isenburg, J. Snoeyink, Mesh collapse compression, in Proceedings of SIBGRAPI’99, Campinas, Brazil (1999), pp. 27–28

  12. M. Isenburg, J. Snoeyink, Face Fixer: Compressing polygon meshes with properties, in Proceedings of ACM SIGGRAPH (2000), pp. 263–270

  13. Z. Karni, C. Gotsman, Spectral compression of mesh geometry, in Proceedings of ACM SIGGRAPH (2000), pp. 279–286

  14. D. King, J. Rossignac, Guaranteed 3.67V bit encoding of planar triangle graphs, in Proceedings of 11th Canadian Conference on Computational Geometry (1999), pp. 146–149

  15. J. Li, C. Kuo, A dual graph approach to 3D triangular mesh compression, in Proceedings of IEEE 1998 International Conference on Image Processing, Chicago, October 4–7 (1998), pp. 891–894

  16. H. Mohimani, M. Babaie-Zadeh, C. Jutten, A fast approach for overcomplete sparse decomposition based on smoothed L0 norm. IEEE Trans. Signal Process. 57(1), 289–301 (2009)

    Article  MathSciNet  Google Scholar 

  17. J. Rossignac, E. Breaker, Connectivity compression for triangle meshes, in Proceedings of IEEE Transactions on Visualization and Computer Graphics (1999), pp. 47–61

  18. G. Taubin, J. Rossignac, Geometric compression through topological surgery. ACM Trans. Graphics 17(2), 84–115 (1998)

    Article  Google Scholar 

  19. C. Touma, C. Gotsman, Triangle mesh compression, in Proceedings of Graphics Interface (1998), pp. 26–34

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61402206) and the Natural Science Fund For Colleges and Universities in Jiangsu Province(13KJB110007).

Author information

Authors and Affiliations

  1. Computer Engineering School, Jiangsu University of Technology, Changzhou, 213001, China

    Zhuo-Ming Du, Fei-Yue Ye, Hang Shi & Guang-Ping Zhu

Authors
  1. Zhuo-Ming Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei-Yue Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hang Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guang-Ping Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhuo-Ming Du.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, ZM., Ye, FY., Shi, H. et al. A Fast Recovery Method of 2D Geometric Compressed Sensing Signal. Circuits Syst Signal Process 34, 1711–1724 (2015). https://doi.org/10.1007/s00034-014-9913-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-014-9913-3

Keywords

Search

Navigation