OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 5 — Mar. 11, 2013
  • pp: 6346–6352

A hybrid method to recognize 3D object

Miao He, Guanglin Yang, and Haiyan Xie  »View Author Affiliations


Optics Express, Vol. 21, Issue 5, pp. 6346-6352 (2013)
http://dx.doi.org/10.1364/OE.21.006346


View Full Text Article

Enhanced HTML    Acrobat PDF (1075 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

A hybrid method using the support vector machine (SVM) correlation filter and the phase-shift interferometry (PSI) holography is proposed to recognize 3D object, which can improve the correct decision rate and resist the distortion of object rotation and noise. The different images of two types of both in-plane and out-of-plane rotated object recorded by digital holography are reconstructed. The reconstructed images of two types are selected to synthesize the SVM correlation filter, respectively. To compare the correct decision rates of the SVM correlation filter with other three ones, it is found that the experimental result is better in rotation resistance and noise tolerance.

© 2013 OSA

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.6890) Image processing : Three-dimensional image processing
(090.1995) Holography : Digital holography
(070.2575) Fourier optics and signal processing : Fractional Fourier transforms
(100.3008) Image processing : Image recognition, algorithms and filters

ToC Category:
Image Processing

History
Original Manuscript: December 14, 2012
Revised Manuscript: February 15, 2013
Manuscript Accepted: February 25, 2013
Published: March 6, 2013

Citation
Miao He, Guanglin Yang, and Haiyan Xie, "A hybrid method to recognize 3D object," Opt. Express 21, 6346-6352 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-5-6346


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. I. Yamaguchi and T. Zhang, “Phase-shifting digital holography,” Opt. Lett.22(16), 1268–1270 (1997). [CrossRef] [PubMed]
  2. E. Tajahuerce, O. Matoba, Y. Frauel, M. A. Castro, and B. Javidi, “New approaches to 3D image recognition,” Proc. SPIE81, 170–185 (2001).
  3. B. Javidi, Image Recognition and Classification: Algorithms, Systems, and Applications (Marcel Dekker, Inc., 2002).
  4. J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, New York, 1996).
  5. B. V. K. V. Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt.31(23), 4773–4801 (1992). [CrossRef] [PubMed]
  6. C. F. Hester and D. Casasent, “Multivariant technique for multiclass pattern recognition,” Appl. Opt.19(11), 1758–1761 (1980). [CrossRef] [PubMed]
  7. B. V. K. V. Kumar, A. Mahalanobis, and R. D. Juday, Correlation Pattern Recognition (Cambridge University, 2005).
  8. I. Kypraios, P. Lei, P. M. Birch, R. C. D. Young, and C. R. Chatwin, “Performance assessment of the modified-hybrid optical neural network filter,” Appl. Opt.47(18), 3378–3389 (2008). [CrossRef] [PubMed]
  9. T. C. Poon, Digital Holography and Three Dimensional Display: Principles and Applications (Springer, New York, 2006), pp. 145–168.
  10. C. M. Bishop, Pattern Recognition and Machine Learning (Springer, 2006).
  11. J. Sun, Q. Li, W. Lu, and Q. Wang, “Image recognition of laser radar using linear SVM correlation filter,” Chin. Opt. Lett.5, 549–551 (2007).
  12. V. N. Vapnik, “An overview of statistical learning theory,” IEEE Trans. Neural Netw.10(5), 988–999 (1999). [CrossRef] [PubMed]
  13. I. Barman, C. R. Kong, N. C. Dingari, R. R. Dasari, and M. S. Feld, “Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose,” Anal. Chem.82(23), 9719–9726 (2010). [CrossRef] [PubMed]
  14. B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, “New support vector algorithms,” Neural Comput.12(5), 1207–1245 (2000). [CrossRef] [PubMed]
  15. B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (MIT, Cambridge, MA, 2002)
  16. I. Barman, N. C. Dingari, N. Rajaram, J. W. Tunnell, R. R. Dasari, and M. S. Feld, “Rapid and accurate determination of tissue optical properties using least-squares support vector machines,” Biomed. Opt. Express2(3), 592–599 (2011). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited