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Object Signature Acquisition through Compressive Scanning

  • Jonathan I. Tamir
  • Dan E. Tamir
  • Shlomi Dolev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7715)

Abstract

In this paper we explore the utility of compressive sensing for object signature generation in the optical domain. We use laser scanning in the data acquisition stage to obtain a small (sub-Nyquist) number of points of an object’s boundary. This can be used to construct the signature, thereby enabling object identification, reconstruction, and, image data compression. We refer to this framework as compressive scanning of objects’ signatures. The main contributions of the paper are the following: 1) we use this framework to replace parts of the digital processing with optical processing, 2) the use of compressive scanning reduces laser data obtained and maintains high reconstruction accuracy, and 3) we show that using compressive sensing can lead to a reduction in the amount of stored data without significantly affecting the utility of this data for image recognition and image compression.

Keywords

Digital Signal Processing Optical Signal Processing Compressive Sensing Shape Representation Object Signature Optical SuperComputing 

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References

  1. 1.
    Frueh, C., Zakhor, A.: Constructing 3D city models by merging ground-based and airborne views. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-562–II-569 (June 2003)Google Scholar
  2. 2.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)Google Scholar
  3. 3.
    Tamir, D.E., Shaked, N.T., Geerts, W.J., Dolev, S.: Compressive sensing of object-signature. In: Dolev, S., Oltean, M. (eds.) OSC 2010. LNCS, vol. 6748, pp. 63–77. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Ye, J.C.: Compressed sensing shape estimation of star-shaped objects in fourier imaging. IEEE Signal Processing Letters 14(10), 750–753 (2007)CrossRefGoogle Scholar
  5. 5.
    Rivenson, Y., Stern, A., Javidi, B.: Compressive fresnel holography. Display Technology. Journal of Display Technology 6(10), 506–509 (2010)CrossRefGoogle Scholar
  6. 6.
    Pavlidis, T.: Algorithms for graphics and image processing. Digital system design series. Computer Science Press (1982)Google Scholar
  7. 7.
    Baggs, R., Tamir, D.E.: Image registration using dynamic space warping. In: Artificial Intelligence and Pattern Recognition 2008, pp. 128–135 (2008)Google Scholar
  8. 8.
    Keogh, E., Wei, L., Xi, X., Hee Lee, S., Vlachos, M.: LB Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In: VLDB, pp. 882–893 (2006)Google Scholar
  9. 9.
    Arkin, E.M., Chiang, Y.J., Held, M., Mitchell, J.S.B., Sacristan, V., Skiena, S.S., Yang, T.C.: On minimum-area hulls (1998)Google Scholar
  10. 10.
    Hug, C.: Extracting artificial surface objects from airborne laser scanner data. In: Automatic Extraction of Man-Made Objects from Aerial and Space Images II, pp. 203–212 (1997)Google Scholar
  11. 11.
    Kirchhoff, S.: Laser diode collimator flatbeam (2011), http://www.sukhamburg.com/onTEAM/pdf/cam_cat_44-45_en.pdf
  12. 12.
    Donoho, D.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Candes, E., Tao, T.: Near-optimal signal recovery from random projections: Universal encoding strategies. IEEE Transactions on Information Theory 52(12), 5406–5425 (2006)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Porat, B.: A course in digital signal processing. John Wiley (1997)Google Scholar
  15. 15.
    Lustig, M., Donoho, D.L., Santos, J.M., Pauly, J.M.: Compressed sensing MRI. IEEE Signal Processing Magazine (2007)Google Scholar
  16. 16.
    Elad, M.: Optimized projections for compressed sensing. IEEE Transactions on Signal Processing 55(12), 5695–5702 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Candes, E., Wakin, M.: An introduction to compressive sampling. IEEE Signal Processing Magazine 25(2), 21–30 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jonathan I. Tamir
    • 1
  • Dan E. Tamir
    • 2
  • Shlomi Dolev
    • 3
  1. 1.Department of Electrical and Computer EngineeringUniversity of Texas at AustinAustinUSA
  2. 2.Department of Computer ScienceTexas State UniversitySan MarcosUSA
  3. 3.Department of Computer ScienceBen-Gurion University of the NegevBeer-ShevaIsrael

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