OSA's Digital Library

Chinese Optics Letters

Chinese Optics Letters

| PUBLISHED MONTHLY BY CHINESE LASER PRESS AND DISTRIBUTED BY OSA

  • Vol. 5, Iss. 12 — Dec. 10, 2007
  • pp: 683–686

A polynomial hybrid reflection model and measurement of its parameters based on images of sample

Lei Yang and Jiuqiang Han  »View Author Affiliations


Chinese Optics Letters, Vol. 5, Issue 12, pp. 683-686 (2007)


View Full Text Article

Acrobat PDF (169 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations
  • Export Citation/Save Click for help

Abstract

Reflectance model is a basic concept in computer vision. Some existing models combining the classical diffuse reflectance model and those for surfaces containing specular components can approximately describe real reflectance. But the ratio of diffuse and specular reflection decided manually has no clear meaning. We propose a new polynomial hybrid reflectance model. The reflectance map equation with a known shape (for example cylinder) as a sample is used to estimate parameters of the proposed reflectance model by least square regression algorithm. Then the reflectance parameters for surfaces of the same class of materials can be determined. Experiments are performed for a metal surface. The synthesis images produced by the proposed method and existing ones are compared with the real acquired image, and the results show that the proposed reflectance model is suitable for describing real reflectance.

© 2007 Chinese Optics Letters

OCIS Codes
(100.2960) Image processing : Image analysis
(120.5820) Instrumentation, measurement, and metrology : Scattering measurements
(160.4760) Materials : Optical properties
(290.5820) Scattering : Scattering measurements
(290.5880) Scattering : Scattering, rough surfaces

Citation
Lei Yang and Jiuqiang Han, "A polynomial hybrid reflection model and measurement of its parameters based on images of sample," Chin. Opt. Lett. 5, 683-686 (2007)
http://www.opticsinfobase.org/col/abstract.cfm?URI=col-5-12-683


Sort:  Year  |  Journal  |  Reset

References

  1. J. F. Blinn, Computer Graphics 11, (2) 192 (1977).
  2. S.-Y. Cho and T. W. S. Chow, Neural Computation 14, 2751 (2002).
  3. S. K. Nayar, K. Ikeuchi, and T. Kanade, IEEE Trans. Pattern Analysis and Machine Intelligence 13, 611 (1991).
  4. G. Bishop and D. M. Weimer, Computer Graphics 20, (4) 103 (1986).
  5. G. J. Ward, Computer Graphics 26, (2) 265 (1992).
  6. K. M. Lee and C.-C. J. Kuo, Computer Vision and Image Understanding 67, 143 (1997).
  7. Q. Zheng and R. Chellappa, IEEE Trans. Pattern Analysis and Machine Intelligence 13, 680 (1991).
  8. S.-Y. Cho and T. W. S. Chow, IEEE Trans. Neural Networks 11, 1498 (2000).
  9. H. D. Tagare and R. J. P. deFigueiredo, IEEE Trans. Pattern Analysis and Machine Intelligence 13, 133 (1991).
  10. C.-T. Lin, W.-C. Cheng, and S.-F. Liang, IEEE Trans. Neural Networks 16, 1601 (2005).
  11. X. Wu, D. Guo, and X. Wang, Computer Engineering and Applications (in Chinese) 24, (19) 23 (2001).
  12. S. R. Marschner, S. H. Westin, E. P. F. Lafortune, and K. E. Torrance, Appl. Opt. 39, 2592 (2000).
  13. R. Basri and D. W. Jacobs, IEEE Trans. Pattern Analysis and Machine Intelligence 25, 218 (2003).
  14. P. Lalonde and A. Fournier, IEEE Trans. Visualization and Computer Graphics 3, 329 (1997).
  15. W. Matusik, H. Pfister, M. Brand, and L. McMillan, ACM Trans. Graphics 22, 759 (2003).
  16. R. Zhang, P.-S. Tsai, J. E. Cryer, and M. Shah, IEEE Trans. Pattern Analysis and Machine Intelligence 21, 690 (1999).
  17. L. Yang and J. Han, Chin. Opt. Lett. 5, 204 (2007).
  18. B. T. Phong, Communications of the ACM 18, 311 (1975).

Cited By

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.

Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited