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Deformable Pedal Curves and Surfaces: Hybrid Geometric Active Models for Shape Recovery

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Abstract

In this paper, we propose significant extensions to the “snake pedal” model, a powerful geometric shape modeling scheme introduced in (Vemuri and Guo, 1998). The extension allows the model to automatically cope with topological changes and for the first time, introduces the concept of a compact global shape into geometric active models. The ability to characterize global shape of an object using very few parameters facilitates shape learning and recognition. In this new modeling scheme, object shapes are represented using a parameterized function—called the generator—which accounts for the global shape of an object and the pedal curve (surface) of this global shape with respect to a geometric snake to represent any local detail. Traditionally, pedal curves (surfaces) are defined as the loci of the feet of perpendiculars to the tangents of the generator from a fixed point called the pedal point. Local shape control is achieved by introducing a set of pedal points—lying on a snake—for each point on the generator. The model dubbed as a “snake pedal” allows for interactive manipulation via forces applied to the snake. In this work, we replace the snake by a geometric snake and derive all the necessary mathematics for evolving the geometric snake when the snake pedal is assumed to evolve as a function of its curvature. Automatic topological changes of the model may be achieved by implementing the geometric snake in a level-set framework. We demonstrate the applicability of this modeling scheme via examples of shape recovery from a variety of 2D and 3D image data.

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References

  • Caselles, V., Catte, F., Coll, T., and Dibos, F. 1993. A geometric model for active contours in image processing. Numerische Mathematik, 66:1-31.

    Google Scholar 

  • Caselles, V., Kimmel, R., and Sapiro, G. 1995. Geodesic active contours. In Fifth International Conference on Computer Vision, pp. 694-699.

  • Gray, A. 1993. Modern Differential Geometry of Curves and Surfaces. CRC Press, Boca Raton.

    Google Scholar 

  • Kass, M., Witkin, A., and Terzopoulos, D. 1987. Snakes: A active contour models. Int. Journal of Computer Vision, 1:321-331.

    Google Scholar 

  • Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., and Yezzi, A. 1995. Gradient flows and geometric active contour models. In Fifth International Conference on Computer Vision, pp. 810-815.

  • Kimmel, R., Amir, A., and Bruckstein, A.M. 1995. Finding shortest paths on surfaces using level sets propogation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 17(6):635-640.

    Google Scholar 

  • Leventon, M.E., Grimson, W.E.L., and Faugeras, O. 2000. Statistical shape influence in geodesic active contours. In Proc. of the IEEE Conference on Compu. Vision and Pattern Recognition (CVPR).

  • Lorigo, L.M., Faugeras, O., Grimson, W.E.L., Keriven, R., Kikinis, R., and Westin, C.F. 1999. Co-dimension 2 geodesic active contours for mra segmentation. In Proc. of the XVI Intl. Conf. on Info. Processing in Medical Imaging, pp. 126-139.

  • Ma, T. and Tagare, H. 1999. Consistency and stability of active contours with euclidean and non-euclidean arc lengths. IEEE Trans. on Image Processing, 8(11): 1549-1559.

    Google Scholar 

  • Malladi, R., Sethian, J.A., and Vemuri, B.C. 1993. A topology independent shape modeling scheme. In SPIE Proc. on Geometric Methods in Computer Vision II, SPIE, Vol. 2031, pp. 246-256.

    Google Scholar 

  • Malladi, R., Sethian, J.A., and Vemuri, B.C. 1995. Shape modeling with front propagation: A level set approach. IEEE Trans. Pattern Analysis and Machine Intelligence, 17(2):158-175.

    Google Scholar 

  • Marc, P., Menet, S., and Medioni, G. 1990. B-snakes: Implementation and application to stereo. In IU Workshop, pp. 720-726.

  • Osher, S. and Sethian, J. 1988. Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulation. Journal of Computational Physics, 79:12-49.

    Google Scholar 

  • Osher, S. and Shu, C.W. 1991. High-order essentially nonoscillatory schemes for Hamilton-Jacobi equations. SIAM J. Numerical Analysis, 28(4):907-922.

    Google Scholar 

  • Paragios, N.K. and Deriche, R. 1998. A pde-based level-set approach for detection of moving objects. In Proc. of the IEEE Intl. Conf. on Compu. Vision, pp. 1139-1145.

  • Shah, J. 1996. A common framework for curve evolution, segmentation and anisotropic diffusion. In IEEE Conf. on Computer Vision and Pattern Recognition, San Francisco, CA.

  • Siddiqi, K., Lauziere, Y.B., Tanenbaum, A., and Zucker, S.W. 1998. Area and length minimizing flows for shape segmentation. IEEE Trans. on Image Processing, 7(3):433-443.

    Google Scholar 

  • Siddiqi, K., Tanenbaum, A., and Zucker, S. 1998. Area and length minimizing flows for shape segmentation. IEEE Trans. on Image Processing, 7(3):433-443.

    Google Scholar 

  • Tek, H. and Kimia, B.B. 1995. Image segmentation by reactiondiffusion bubbles. In Fifth International Conference on Computer Vision, Boston, MA.

  • ter Harr Romeny, B.M. (Ed.). 1994. Geometry-Driven Diffusion in Computer Vision. Computational Imaging and Vision, Kluwer Academic Publishers, The Netherlands.

  • Vemuri, B.C. and Guo, Y. 1998. Snake pedals: Geometric models with physics-based control. In Proc. of the IEEE International Conference on Computer Vision, Bombay, India, pp. 427-432.

  • Vemuri, B.C. and Guo, Y. 2000. Snake pedals: Compact and versatile geometric models with physics-based control. In IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(5):445-459.

    Google Scholar 

  • Vemuri, B.C. and Radisavljevic, A. 1994. Multiresolution stochastic hybrid shape models with fractal priors. In ACM Trans. on Graphics, pp. 177-207.

  • Wickert, J. 1996. Nonlinear diffusion in scale space: From the continuous to the discrete setting. In Proc. ICAOS'96: Images, Wavelets and PDE's, Berger, M.O. (Ed.), Springer, New York, Vol. 219, pp. 111-118.

    Google Scholar 

  • Xu, C. and Prince, J. 1998. Snakes, shapes and gradient vector flow. IEEE Trans. on Image Processing, 7(3):359-369.

    Google Scholar 

  • Yezzi, A., Tsai, A., and Wilsky, A. 1999. A statistical approach to snakes for bimodal and trimodal imagery. In Proc. of the IEEE Intl. Conf. on Compu. Vision, pp. 898-903.

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Vemuri, B.C., Guo, Y. & Wang, Z. Deformable Pedal Curves and Surfaces: Hybrid Geometric Active Models for Shape Recovery. International Journal of Computer Vision 44, 137–155 (2001). https://doi.org/10.1023/A:1011897628647

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