Skip to main content
SpringerLink
Log in
Book cover

Optical Imaging Sensors and Systems for Homeland Security Applications pp 97–114Cite as

3D Object Recognition using Gabor Feature Extraction and PCA-FLD Projections of Holographically Sensed Data

  • Chapter

  • 653 Accesses

Part of the book series: Advanced Sciences and Technologies for Security Applications ((ASTSA,volume 2))

5.7 Conclusions

In this research, a 3D object classification technique using a single hologram has been presented. The PCA-FLD classifier with feature vectors based on Gabor wavelets has been utilized for this purpose. Training and test data of the 3D objects were obtained by computational holographic imaging. We were able to classify 3D objects used in the experiments with a few reconstructed planes of the hologram. The Gabor approach appears to be a good feature extractor for hologram-based 3D classification. The FLD combined with the PCA proved to be a very efficient classifier even with a few training data. Substantial dimensionality reduction was achieved by using the proposed technique for 3D classification problem using holographic imaging. As a consequence, we were able to classify different classes of 3D objects using computer-reconstructed holographic images.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Rosen J. (1998). “Three-dimensional joint transform correlator.” Appl. Opt., 37(32):7538–7544.

    Article  ADS  Google Scholar 

  2. Esteve-Taboada JJ, Mas D, and Garcia J. (1999). “Three-dimensional object recognition by Fourier transform profilometry.” Appl. Opt., 38(22):4760–4765.

    Article  ADS  Google Scholar 

  3. Goudail F and Refregier P. (2001). “Statistical algorithms for target detection in coherent active polarimetric images.” J. Opt. Soc. Am., 18(2):3049–3060.

    Article  ADS  Google Scholar 

  4. Sadjadi FA. (2002). “New results in the use of polarization diversity for classification of radar targets.” in Automatic Target Recognition XII, FA. Sadjadi, ed., Proc. SPIE 4726, pp. 26–34.

    Google Scholar 

  5. Javidi B, ed. (2002). Image Recognition and Classification: Algorithms, Systems, and Applications. Marcel Dekker, New York.

    MATH  Google Scholar 

  6. Javidi B and Tajahuerce E. (2000). “Three-dimensional object recognition by use of digital holography.” Opt. Lett., 25(9):610–612.

    Article  ADS  Google Scholar 

  7. Frauel Y, Tajahuerce E, Castro M, and Javidi B. (2001). “Distortion-tolerant three-dimensional object recognition with digital holography.” Appl. Opt., 40(23):3887–3893.

    Article  ADS  Google Scholar 

  8. Frauel Y and Javidi B. (2001). “Neural network for three-dimensional object recognition based on digital holography.” Opt. Lett., 26(9):1478–1480.

    Article  ADS  Google Scholar 

  9. Frauel Y and Javidi B. (2002). “Digital three-dimensional image correlation by use of computer-reconstructed integral imaging.” Appl. Opt., 41(26):5488–5496.

    Article  ADS  Google Scholar 

  10. Perona MT, Mahalanobis A, and Norris-Zachery K. (1999). “Ladar automatic target recognition using correlation filters.” in Automatic Target Recognition IX, FA. Sadjadi, ed., Proc. SPIE 3718, pp. 388–396.

    Google Scholar 

  11. Perona MT, Mahalanobis A, and Norris-Zachery K. (2000). “System-level evaluation of ladar ATR using correlation filters.” in Automatic Target Recognition X, FA. Sadjadi, ed., Proc. SPIE 4050, pp. 69–75.

    Google Scholar 

  12. Mahalanobis, (1996). “Review of correlation filters and their application for scene matching.” in Optoelectronic Devices and Systems for Processing, B Javidi; KM Johnson, eds., Proc. SPIE CR65, pp. 240–260.

    Google Scholar 

  13. Yeom S and Javidi B. (2004). “Three-dimensional object feature extraction and classification with computational holographic imaging.” Appl. Opt., 43(2):442–451.

    Article  ADS  Google Scholar 

  14. Duda RO, Hart PE, and Stork DG. (2001). Pattern Classification 2nd edn. Wiley, New York.

    MATH  Google Scholar 

  15. Daugman JG. (1985). “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.” J. Opt. Soc. Am., 2(7):1160–1169.

    Article  ADS  Google Scholar 

  16. Daugman JG. (1988). “Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression.” IEEE Trans. ASSP., 36(7):1169–1179.

    Article  MATH  Google Scholar 

  17. Lee TS. (1996). “Image representation using 2-D Gabor wavelets.” IEEE Trans. PAMI., 18(10):959–971.

    Google Scholar 

  18. Belhumer PN, Hespanha JP, and Kriegman DJ. (1997). “Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection.” IEEE Trans. PAMI., 19(7):711–720.

    Google Scholar 

  19. Swets DL and Weng J. (1996). “Using discriminant eigenfeatures for image retrieval.” IEEE Trans. PAMI., 18(8):831–836.

    Google Scholar 

  20. Lyons MJ, Budynek J, and Akamatsu S. (1999). “Automatic classification of single facial images.” IEEE Trans. PAMI., 21(12):1357–1362.

    Google Scholar 

  21. Liu C and Wechsler H. (2002). “Gabor feature based classification using the enhanced Fisher Linear Discriminant model for face recognition.” IEEE Trans. Image Processing, 11(4):467–476.

    Article  ADS  Google Scholar 

  22. Yamaguchi I and Zhang T. (1997). “Phase-shifting digital holography.” Opt. Lett., 22(16):1268–1270.

    Article  ADS  Google Scholar 

  23. Lades M, Vorbruggen JC, Buhmann J, Lange J, Christoph v.d. Malsburg, Wurtz RP, and Konen W. (1993). “Distortion invariant object recognition in the dynamic link architecture.” IEEE Trans. Comput., 42(3):300–311.

    Article  Google Scholar 

  24. Yeom S, Javidi B, Roh YJ, and Cho HS. (2004). “Three-dimensional object recognition using X-ray imaging.” Submitted to Opt. Eng.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, U-2157, University of Connecticut, Storrs, CT, 06269-2157, USA

    Seokwon Yeom & Bahram Javidi

Authors
  1. Seokwon Yeom
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bahram Javidi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Electrical & Computer Engineering Dept., University of Connecticut, 371 Fairfield Road, Unit 1157, Storrs, CT, 06269-1157, USA

    Bahram Javidi (Distinguished Professor of Electrical and Computer Engineering) (Distinguished Professor of Electrical and Computer Engineering)

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

Yeom, S., Javidi, B. (2006). 3D Object Recognition using Gabor Feature Extraction and PCA-FLD Projections of Holographically Sensed Data. In: Javidi, B. (eds) Optical Imaging Sensors and Systems for Homeland Security Applications. Advanced Sciences and Technologies for Security Applications, vol 2. Springer, New York, NY. https://doi.org/10.1007/0-387-28001-4_5

Download citation

Publish with us

Policies and ethics

Search

Navigation