3D shape from anisotropic diffusion

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 1, Issue , 2003, Pages

  Favaro, P ^a   Osher, S ^b   Soatto, S ^c   Vese, L ^b  

^a WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c University of California   (United States)

Author keywords

Anisotropic diffusion; Depth from defocus; Early vision; Image based modeling; Shape from defocus; Shape representation

Indexed keywords

ALGORITHMS; DIFFUSION; IMAGE SEGMENTATION; INVERSE PROBLEMS; MATHEMATICAL MODELS; PARTIAL DIFFERENTIAL EQUATIONS; THREE DIMENSIONAL;

ANISOTROPIC DIFFUSION; BLIND DECONVOLUTION; HEAT EQUATION; THREE DIMENSIONAL SHAPE;

PATTERN RECOGNITION;

EID: 0041438377     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. M. J. Black, G. Sapiro, D. H. Marimont, and D. Heeger. Robust anisotropic diffusion. IEEE Transactions on Image Processing, 7(3):421-32, 1998.
2. T. Chan and L. Vese. Active contours without edges. IEEE Transactions on Image Processing, 10(2):266-77, 2001.
3. S. Chaudhuri and A. Rajagopalan. Depth from defocus: a real aperture imaging approach. Springer Verlag, 1999.
4. J. Ens and P. Lawrence. An investigation of methods for determining depth from focus. IEEE Trans. Pattern Anal. Mach. Intell., 15:97-108, 1993.
5. P. Favaro, S. Osher, L. Vese, and S. Soatto. 3d shape from anisotropic diffusion. In Technical Report UCLA, January 2003.
6. P. Favaro and S. Soatto. Shape and reflectance estimation from the information divergence of blurred images. In European Conference on Computer Vision, pages 755-768, June 2000.
7. B. Fischl and E. L. Schwartz. Learning an integral equation approximation to nonlinear anisotropic diffusion in image processing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4):342-52, 1997.
8. H. Jin and P. Favaro. A variational approach to shape from defocus. In ECCV (2), pages 18-30, 2002.
9. R. Lagnado and S. Osher. A technique for calibrating derivative security pricing models: numerical solution of an inverse problem. J. Comput. Finance, 1(1):13-26, 1997.
10. S. Osher and S. Sethian. Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulation. J. of Comput. Phys., 79:12-49, 1998.
11. A. Pentland. A new sense for depth of field. IEEE Trans. Pattern Anal. Mach. Intell., 9:523-531, 1987.
12. P. Perona and J. Malik. Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-39, 1990.
13. S. Soatto and P. Favaro. A geometric approach to blind deconvolution with application to shape from defocus. In Intl. Conf. Computer Vision and Pattern Recognition, pages 10-17, June 2000.
14. M. Subbarao and G. Surya. Depth from defocus: a spatial domain approach. Intl. J. of Computer Vision, 13:271-294, 1994.
15. M. Watanabe and S. Nayar. Rational filters for passive depth from defocus. Intl. J. of Comp. Vision, 27(3):203-225, 1998.
16. J. Weickert. Anisotropic Diffusion in Image Processing. B.G.Teubner Stuttgart, 1998.
17. Y. Xiong and S. Shafer. Depth from focusing and defocusing. In Proc. of the Intl. Conf. of Comp. Vision and Pat. Recogn., pages 68-73, 1993.