Flows under min/max curvature flow and mean curvature: Applications in image processing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1064, Issue , 1996, Pages 251-262

  Malladi, R ^a   Sethian, J A ^a  

^a LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; TEXTURES;

BLACK AND WHITE IMAGES; CURVES AND SURFACES; GREY SCALE IMAGES; IMAGE PROCESSING APPLICATIONS; ISO-INTENSITY CONTOURS; LEVEL SET FORMULATIONS; PROFILE EVOLUTION; SALT AND PEPPERS;

IMAGE ENHANCEMENT;

EID: 84937061976     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0015541     Document Type: Conference Paper

References (18)

1. L. Alvarez, P. L. Lions, and J. M. Morel, “Image selective smoothing and edge detection by nonlinear diffusion. II,” SIAM Journal on Numerical Analysis, Vol. 29(3), pp. 845-866, 1992.
2. J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. PAMI-8, pp. 679-698, 1986.
3. M. Gage, “Curve shortening makes convex curves circular,” Inventiones Mathematica, Vol. 76, pp. 357, 1984.
4. M. Grayson, “The heat equation shrinks embedded plane curves to round points,” J. Diff. Geom., Vol. 26, pp. 285-314, 1987.
5. R. Malladi and J. A. Sethlan, “Image processing via level set curvature flow,” Proc. Natl. Acad. of Sei., USA, Vol. 92, pp. 7046-7050, July 1995.
6. R. Malladi and J. A. Sethian, “Image processing: Flows under min/max curvature and mean curvature,” to appear in Graphical Models and Image Processing, March 1996.
7. R. Malladi and J. A. Sethian, “A unified approach for shape segmentation, representation, and recognition,” Report LBL-36069, Lawrence Berkeley Laboratory, University of California, Berkeley, August 1994.
8. R. Malladi, D. Adalsteinsson, and J. A. Sethian, “Fast method for 3D shape recovery using level sets,” submitted.
9. R. Malladi, J. A. Sethian, and B. C. Vemuri, “Evolutionary fronts for topologyindependent shape modeling and recovery,” in Proceedings of Third European Conference on Computer Vision, LNCS Vol. 800, pp. 3-13, Stockholm, Sweden, May 1994.
10. R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: A level set approach,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 17(2), pp. 158-175, Feb. 1995.
11. D. Marr and E. Hildreth, “A theory of edge detection,” Proc. of Royal Soc. (London), Vol. B207, pp. 187-217, 1980.
12. S. Osher and J. A. Serbian, “Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulation,” Journal of Computational Physics, Vol. 79, pp. 12-49, 1988.
13. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 12(7), pp. 629-639, July 1990.
14. L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Modelisations Matematiques pour le traitement d'images, INRIA, pp. 149-179, 1992.
15. G. Sapiro and A. Tannenbaum, “Image smoothing based on affine invariant flow,” Proc. of the Conference on Information Sciences and Systems, Johns Hopkins University, March 1993.
16. J. A. Sethian, “Curvature and the evolution of fronts,” Commun. in Mathematical Physics, Vol. 101, pp. 487-499, 1985.
17. J. A. Sethian, “Numerical algorithms for propagating interfaces: Hamilton-Jacobi equations and conservation laws,” Journal of Differential Geometry, Vol. 31, pp. 131-161, 1990.
18. J. A. Serbian, “Curvature flow and entropy conditions applied to grid generation,” Journal of Computational Physics, Vol. 115, No. 2, pp. 440-454, 1994.