Bibliography

[1] Caselles, V., Catte, F., Coll, T., and Dibos, F., A geometric model for active contours, Numer. Math., Vol. 66, pp. 1-31, 1993.

[2] Malladi, R., Sethian, J., and Vemuri, B., Evolutionary fronts for topology independent shape modeling and recovery, In: Proceedings of the 3rd European Conference on Computer Vision, pp. 3-13, 1994.

[3] Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., and Yezzi, A., Gradient flows and geometric active contour models, In: Proceedings of the 5th IEEE International Conference on Computer Vision, pp. 810815, 1995.

[4] Caselles, V., Kimmel, R., and Sapiro, G., Geodesic active contour, International J. Comput. Vis., Vol. 22, No. 1, pp. 61-79, 1997.

[5] Malladi, R., Sethian, J., and Vemuri, B., Shape modeling with front propagation: A level set approach, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 17, No. 2, pp. 158-175, 1995.

[6] Sapiro, G., Color Snakes, Comput. Vis. Image Underst., Vol. 68, No. 2, pp. 247-253, 1997.

[7] Xu, C. and Prince, J., Snakes, Shapes, and Gradient Vector Flow, IEEE Trans. Image Process., Vol. 7, No. 3, pp. 359-369, 1998.

[8] Xu, C. and Prince, J., Gradient vector flow: A new external force for snakes, In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, pp. 66-71, 1997.

[9] Xu, C. and Prince, J., generalized gradient vector flow external forces for active contours, Signal Process., Vol. 71, No. 2, pp. 131-139, 1998.

[10] Xu, C., Yezzi, J., and Prince, J., On the relationship between parametric and geometric active contours, In: Proceedings of the 34th Asilomar Conference on Signal, Systems, and Computers, pp. 483-489, 2000.

[11] Chan, T. andVese, L., Active contours without edges, IEEE Trans. Image Process., Vol. 10, No. 2, pp. 266-277, 2001.

[12] Comaniciu, D. and Meer, P., Mean shift analysis and applications, In: Proceedings of the 7th IEEE International Conference on Computer Vision, pp. 1197-1203, 1999.

[13] Comaniciu, D. and Meer, P., Mean shift: A robust approach toward feature space analysis, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 24,

[14] Siddiqi, K., Lauziere, Y., Tannenbaum, A., andZucker, S., Area and length minimizing flows for shape segmentation, IEEE Trans. Image Process., Vol. 7, No. 3, pp. 433-443, 1998.

[15] Osher, S. and Sethian, J., Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys., Vol. 79, pp. 12-49, 1988.

[16] Sethian, J., Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge University Press, Cambridge, 1996.

[17] Sethian, J., Curvature and the evolution of fronts, Commun. Math. Phys., Vol. 101, pp. 487-499, 1985.

[18] Osher, S. and Fedkiw, R., Level Sets and Dynamic Implicit Surfaces, Springer-Verlag, New York, 2002.

[19] di Zenzo, S., A note on the gradient of a multi-image, Comput. Vis., Graph. Image Process., Vol. 33, No. 1, pp. 116-125, 1986.

[20] Comaniciu, D. and Meer, P., Robust analysis of feature spaces: Color image segmentation, In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, pp. 750-755, 1997.

[21] Cootes, T., Taylor, C., Cooper, D., and Graham, J., Active shape models— their training and application, Comput. Vis. Image Underst., Vol. 61, No. 1, pp. 38-59, 1995.

[22] Osareh, A., Mirmehdi, M., Thomas, B., and Markham, R., Colour morphology and snakes for optic disc localisation, In: Proceedings of the 6th Conference on Medical Image Understanding and Analysis, pp. 21-24, 2002.

[23] Blake, A. and Isard, M., Active Contours, Springer, London, 1998.

[24] Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active contour models, Int. J. Comput. Vis., Vol. 1, pp. 321-331, 1988.

[25] Ronfard, R., Region-based strategies for active contour models, int. J. Comput. Vis., Vol. 13, No. 2, pp. 229-251, 1994.

[26] Chakraborty, A., Staib, L., and Duncan, J., Deformable boundary finding in medical images by integrating gradient and region information, IEEE Trans. Med. Imaging, Vol. 15, No. 6, pp. 859-870, 1996.

[27] Zhu, S. and Yuille, A., Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 18, No. 9, pp. 884-900, 1996.

[28] Paragios, N. and Deriche, R., Coupled geodesic active regions for image segmentation: A level set approach, In: Proceedings of the 6th European Conference on Computer Vision, pp. 224-240, 2000.

[29] Paragios, N. and Deriche, R., Geodesic active regions: A new framework to deal with frame partition problems in computer vision, J. Vis. Commun. Image Represent., Vol. 13, No. 1-2, pp. 249-268, 2002.

[30] Yezzi, A., Tsai, A., and Willsky, A., A fully global approach to image segmentation via coupled curve evolution equations, J. Vis. Commun. Image Represent., Vol. 13, No. 1-2, pp. 195-216, 2002.

[31] Chop, D., Computing minimal surfaces via level set curvature-flow, J. Comput. Phys., Vol. 106, pp. 77-91, 1993.

[32] Adalsterinsson, D. and Sethian, J., A fast level set method for propagating interfaces, J. Comput. Phys., Vol. 118, pp. 269-277, 1995.

[33] Sethian, J., Theory, Algorithms, and Applications of Level Set Methods for Propagating Interfaces, Acta Numer., Vol. 5, pp. 309-395, 1996.

[34] Sethian, J., A fast marching level set method for monotonically advancing fronts, In: Proceedings of the National Academy of Sciences, Vol. 93, pp. 1591-1694, 1996.

[35] Paragios, N. and Deriche, R., Geodesic active contour and level set for the detection and tracking of moving objects, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 22, No. 3, pp. 266-280, 2000.

[36] Milne, R., An adaptive level-set method, Ph.D. Thesis, Department of Mathematics, University of California, Berkeley, 1995.

[37] Weickert, J., ter Harr Romeny, B. M., and Viergener, M., Efficient and reliable scheme for non-linear diffusion and filtering, IEEE Trans. Image Process., Vol. 7, pp. 398-410, 1998.

[38] Goldenberg, R., Kimmel, R., Rivlin, E., and Rudzsky, M., Fast geodesic active contours, IEEE Trans. Image Process., Vol. 10, No. 10, pp. 14671475, 2001.

[39] Fukunaga, K. and Hostetler, L., The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Trans. Inf. Theory, Vol. IT-21, pp. 32-40, 1975.

[40] Cheng, Y., Mean shift, mode seeking and clustering, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 17, No. 8, pp. 790-799, 1995.

[41] Danielsson, P., Euclidean distance mapping, Comput. Graph. Image Process., Vol. 14, pp. 227-248, 1980.

[42] Borgefors, G., Distance transformations in arbitrary dimensions, Comput. Vis., Graph. Image Process., Vol. 27, pp. 321-345, 1984.

[43] Eggers, H., Two fast Euclidean distance transformations in Z2 based on sufficient propagation, Comput. Vis. Image Underst., Vol. 69, No. 1, pp. 106-116, 1998.

[44] Gevers, T., Ghebreab, S., and Smeulders, A., Color invariant snakes, In: Proceedings of the 9th British Machine Vision Conference, pp. 659-670, 1998.

[45] Osareh, A., Mirmehdi, M., Thomas, B., and Markham, R., Identification of exudate pathologies and the optic disc in colour retinal images, Br. J. Ophthalmol., Vol. 87, pp. 1220-1223, 2003.

Was this article helpful?

0 0

Post a comment