Biomedical Engineering Reference
In-Depth Information
[53] Carstensen, J. M. and Fisker, R., On parameter estimation in de-
formable models, In: Fourteenth International Conference on Pattern
Recognition, August 16-20, pp. 762-766, 1998.
[54] Rowley, Henry, Baluja, Shumeet, and Kanade, Takeo, Neural network-
based face detection. In: Computer Vision and Pattern Recognition '96,
June 1996.
[55] Rumelhart, D. E., Hinton, G., and Williams, R., Learning represen-
tations by back-propagation errors, Nature, Vol. 323, pp. 533-536,
1986.
[56] Sapiro, G., Color snakes. Technical Report, Hewlett-Packard Labs,
guille@hpl.hp.com, 1995.
[57] Sapiro, G., Color snakes. Comput. Vis. Image Underst., Vol. 68, No. 2,
pp. 247-253, 1997.
[58] Sapiro, G. and Tannenbaum, A., Affine invariant scale-space, Int. J.
Comput. Vis., Vol. 11, No. 1, pp. 25-44, 1993.
[59] Sarti, A., Ortiz, C., Lockett, S., and Malladi, R., A unified geometric
model for 3d confocal image analysis in cytology. In Proc. Interna-
tional Symposium on Computer Graphics, Image Processing, and Vi-
sion (SIBGRAPI'98), pp. 69-76, 1998.
[60] Schroeder, W., Martin, K., and Lorensen, B., The Visualization Toolkit:
An Object-Oriented Approach To 3D Graphics, Prentice-Hall PTR, En-
glewood Cliffs, NJ, 1998.
[61] Sethian, J. A., Level Set Methods: Evolving Interfaces in Geometry,
Fluid Mechanics, Computer Vision and Materials Sciences, Cambridge
University Press, Cambridge 1996.
[62] Storvik, G., A Bayesian approach to dynamic contours through
stochastic sampling and simulated annealing, IEEE Trans. Pattern
Anal. Mach. Intell., Vol. 16, No. 10, pp. 976-986, 1994.
[63] Strauss, E., Jimenez, W., Giraldi, G. A., Silva, R., and Oliveira, A. F., A
semi-automatic surface reconstruction framework based on t-surfaces
and isosurface extraction methods. In: International Symposium on
