OSA's Digital Library

Chinese Optics Letters

Chinese Optics Letters

| PUBLISHED MONTHLY BY CHINESE LASER PRESS AND DISTRIBUTED BY OSA

  • Vol. 5, Iss. 4 — Apr. 10, 2007
  • pp: 204–207

Fast algebra algorithm of shape-from-shading with specular reflectance

Lei Yang and Jiuqiang Han  »View Author Affiliations


Chinese Optics Letters, Vol. 5, Issue 4, pp. 204-207 (2007)


View Full Text Article

Acrobat PDF (189 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

Shape-from-shading (SFS) is to reconstruct three-dimensional (3D) shape from a single gray image, which is an important problem in computer vision. We propose a novel SFS method based on hybrid reflection model which contains both diffuse reflectance and specular reflectance. The intensity gradient of image is in the direction that the shape of surface changes most, so we use directional derivative of the reflectance map as parts of objective function. When discrete characteristic of digital images is considered, finite difference approximates differential operator. So the reflectance map equation described by a partial differential equation (PDE) turns into an algebra equation about the unknown surface height correspondingly. Using iterative numeric computation, a new SFS method is gained. Experiments on synthesis and real images show that the proposed SFS method is accurate and fast.

© 2007 Chinese Optics Letters

OCIS Codes
(100.5010) Image processing : Pattern recognition
(110.6150) Imaging systems : Speckle imaging

Citation
Lei Yang and Jiuqiang Han, "Fast algebra algorithm of shape-from-shading with specular reflectance," Chin. Opt. Lett. 5, 204-207 (2007)
http://www.opticsinfobase.org/col/abstract.cfm?URI=col-5-4-204


Sort:  Author  |  Year  |  Journal  |  Reset

References

  1. B. K. P. Horn, in P. H. Winston, (ed.) The Psychology of Computer Vision (McGraw-Hill, New York, 1975) Chap.4, pp.115-155.
  2. B. K. P. Horn, Int. J. Computer Vision 5, 37 (1990).
  3. R. Zhang, P.-S. Tsai, J. E. Cryer, and M. Shah, IEEE Trans. Pattern Analysis and Machine Intelligence 21, 690 (1999).
  4. S.-Y. Cho and T. W. S. Chow, Neural Computation 14, 2751 (2002).
  5. J.-D. Durou, M. Falcone, and H. Sagona, IRIT Research Report 2004-2-R (2004).
  6. Y. Hu, B. Liu, F. Li, and C. Meng, Acta Photon. Sin. (in Chinese) 32, 985 (2003).
  7. C.-T. Lin, W.-C. Cheng, and S.-F. Liang, IEEE Trans. Neural Networks 16, 1601 (2005).
  8. A. Tankus, N. Sochen, and Y. Yeshurun, Int. J. Computer Vision 63, 21 (2005).
  9. R. Kimmel and J. A. Sethian, J. Mathematical Imaging and Vision 14, 237 (2001).
  10. L. Song, X. Qu, K. Xu, and L. Lu, NDT-E International 38, 381 (2005).
  11. A. G. Bors, E. R. Hancock, and R. C. Wilson, IEEE Trans. Pattern Analysis and Machine Intelligence 25, 974 (2003).
  12. H. D. Tagare and R. J. P. deFigueiredo, IEEE Trans. Pattern Analysis and Machine Intelligence 13, 133 (1991).
  13. R. Zhang and M. Shah, IEEE Trans. Systems Man and Cybernetics A 29, 318 (1999).

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.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited