OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 7, Iss. 1 — Jan. 4, 2012

3D video visualization employing wavelet multilevel decomposition

Eduardo Ramos-Diaz and Volodymyr Ponomaryov  »View Author Affiliations


Applied Optics, Vol. 50, Issue 32, pp. 6084-6091 (2011)
http://dx.doi.org/10.1364/AO.50.006084


View Full Text Article

Enhanced HTML    Acrobat PDF (805 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

This study analyzed the implementation and performance of a framework that can be efficiently applied to three-dimensional (3D) video sequence visualization. The proposed algorithm is based on wavelets and wavelet atomic functions used in the computation of disparity maps. The proposed algorithm employs wavelet multilevel decomposition and 3D visualization via color anaglyphs synthesis. Simulations were run on synthetic images, synthetic video sequences, and real-life video sequences. Results shows that this novel approach performs better in depth and spatial perception tasks compared to existing methods, both in terms of objective criteria such as quantity of bad disparities and similarity structural index measure and the more subjective measure of human vision.

© 2011 Optical Society of America

OCIS Codes
(100.7410) Image processing : Wavelets
(330.4150) Vision, color, and visual optics : Motion detection
(110.4155) Imaging systems : Multiframe image processing
(110.3010) Imaging systems : Image reconstruction techniques

ToC Category:
Image Processing

History
Original Manuscript: April 25, 2011
Revised Manuscript: July 12, 2011
Manuscript Accepted: July 13, 2011
Published: November 4, 2011

Virtual Issues
Vol. 7, Iss. 1 Virtual Journal for Biomedical Optics

Citation
Eduardo Ramos-Diaz and Volodymyr Ponomaryov, "3D video visualization employing wavelet multilevel decomposition," Appl. Opt. 50, 6084-6091 (2011)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=ao-50-32-6084


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. J. L. Barron, D. J. Fleet, S. Beauchemin, and T. Burkitt, “Performance of optical flow techniques,” in Conference on Computer Vision and Pattern Recognition (IEEE, 1992), pp. 236–242.
  2. D. Fleet and A. Jepson, Measurement of Image Velocity (Kluwer, 1992). [CrossRef]
  3. B. B. Alagoz, “Obtaining depth maps from color images by region based stereo matching algorithms,” OncuBilim Algorithm and Systems Labs 8, 1–12 (2008).
  4. A. Bhatti and S. Nahavandi, 2008 Stereo Vision (I-Tech, 2008).
  5. E. Dubois, X. Huang, “3D reconstruction based on a hybrid disparity estimation algorithm,” in International Conference on Image Processing (IEEE, 2006), pp. 1025–1028.
  6. Y. Meyer, Ondelettes (Hermann, 1991).
  7. C. Juarez, V. Ponomaryov, J. Sanchez, and V. Kravchenko, “Wavelets based on atomic function used in detection and classification of masses in mammography,” in Lecture Notes in Artificial Intelligence (Springer, 2008), Vol.  LNAI 5317, pp. 295–304.
  8. V. Ponomaryov and E. Ramos, “3D video sequence reconstruction algorithms implemented on a DSP,” Proc. SPIE 7871, 78711D (2011). [CrossRef]
  9. V. Kravchenko, H. Meana, V. Ponomaryov, 2009 Adaptive Digital Processing of Multidimensional Signals with Applications (FizMatLit, 2009). Available at http://www.posgrados.esimecu.ipn.mx/.
  10. Y. Gulayev, V. Kravchenko, and V. Pustoviot, “A new class of WA-systems of Kravchenko-Rvachev functions,” Dokl., Math. 75, 325–332 (2007). [CrossRef]
  11. M. Unser and A. Aldroubi, “A general sampling theorem for nonideal acquisition devices,” IEEE Trans. Signal Process. 42, 2915–2925 (1994). [CrossRef]
  12. W. Sanders and D. McAllister, “Producing anaglyphs from synthetic images,” Proc. SPIE 5006, 348–358 (2003). [CrossRef]
  13. I. Ideses and L. Yaroslavsky, “Three methods that improve the visual quality of color anaglyphs,” J. Opt. A 7, 755–762(2005). [CrossRef]
  14. P. Yaroslavsky, J. Campos, M. Espínola, and I. Ideses, “Redundancy of stereoscopic images: experimental evaluation,” Opt. Express 13, 10895–10907 (2005). [CrossRef] [PubMed]
  15. I. Idesses, L. Yaroslavsky, and B. Fishbain, “3D from compressed 2D video,” Proc. SPIE 6490, 64901C (2007). [CrossRef]
  16. I. Idesses and L. Yaroslavsky, “A method for generating 3D video from a single video stream,” in VMV (Aka GmbH, 2002), pp. 435–438.
  17. Middlebury College, “Middlebury stereo datasets,” http://vision.middlebury.edu/stereo/data.
  18. Arizona State University, “YUV video sequences,” http://trace.eas.asu.edu/yuv/index.html.
  19. W. S. Malpica and A. C. Bovik, “Range image quality assessment by structural similarity,” in IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2009), pp. 1149–1152. [CrossRef]
  20. Z. Wang and A. C. Bovik, “Mean squared error: love it or leave it? A new look at signal fidelity measures,” IEEE Signal Process. Mag. 26, 98–117 (2009). [CrossRef]

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