Understanding and modeling the evolution of critical points under gaussian blurring

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

Volumn 2350, Issue , 2002, Pages 143-157

  Kuijper, Arjan ^a   Florack, Luc ^b  

^a UTRECHT UNIVERSITY   (Netherlands)

^b EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

MAGNETIC RESONANCE IMAGING;

ARTIFICIAL IMAGE; CATASTROPHE THEORY; GAUSSIAN SCALE SPACE; MATHEMATICAL FRAMEWORKS; PARAMETER-DRIVEN; SCALE SPACE SADDLES; SCALE-SPACE HIERARCHIES; SPECIAL POINTS;

COMPUTER VISION;

EID: 84944382803     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47969-4_10     Document Type: Conference Paper

References (16)

1. V. I. Arnold. Catastrophe Theory. Springer, Berlin, 1984.
2. J. Damon. Local Morse theory for solutions to the heat equation and Gaussian blurring. Journal of Differential Equations, 115(2):386–401, 1995.
3. L. M. J. Florack and A. Kuijper. The topological structure of scale-space images. Journal of Mathematical Imaging and Vision, 12(1):65–80, February 2000.
4. L. M. J. Florack, B. M. ter Haar Romeny, J. J. Koenderink, and M. A. Viergever. Cartesian differential invariants in scale-space. Journal of Mathematical Imaging and Vision, 3(4):327–348, 1993.
5. R. Gilmore. Catastrophe Theory for Scientists and Engineers. Dover, 1993. Originally published by JohnWiley & Sons, NewYork, 1981.
6. L. D. Griffin and A. Colchester. Superficial and deep structure in linear diffusion scale space: Isophotes, critical points and separatrices. Image and Vision Computing, 13(7):543–557, September 1995.
7. P. Johansen, M. Nielsen, and O.F. Olsen. Branch points in one-dimensional Gaussian scale space. Journal of Mathematical Imaging and Vision, 13:193–203, 2000.
8. J. J. Koenderink. The structure of images. Biological Cybernetics, 50:363–370, 1984.
9. J. J. Koenderink. A hitherto unnoticed singularity of scale-space. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(11):1222–1224, 1989.
10. A. Kuijper and L. M. J. Florack. Hierarchical pre-segmentation without prior knowledge. In Proceedings of the 8th International Conference on Computer Vision (Vancouver, Canada, July 9–12, 2001), pages 487–493, 2001.
11. L. M. Lifshitz and S. M. Pizer. Amultiresolution hierarchical approach to image segmentation based on intensity extrema. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(6):529–540, 1990.
12. T. Lindeberg. Scale-Space Theory in Computer Vision. The Kluwer International Series in Engineering and Computer Science. Kluwer Academic Publishers, 1994.
13. A. Simmons, S.R. Arridge, P.S. Tofts, and G.J. Barker. Application of the extremum stack to neurological MRI. IEEE Transactions on Medical Imaging, 17(3):371–382, June 1998.
14. R. Thom. Stabilité Structurelle et Morphogénèse. Benjamin, New york, 1972.
15. T. Wada and M. Sato. Scale-space tree and its hierarchy. In ICPR90, pages Vol–II 103–108, 1990.
16. A.P. Witkin. Scale-space filtering. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pages 1019–1022, 1983.