1. Evolving Curves/Surfaces for Geometric Reconstruction and Image Segmentation Huaiping Yang (Joint work with Bert Juettler) Johannes Kepler University of Linz Workshop on Algebraic Spline Curves and Surfaces, May 17-18, 2006, Eger, Hungary

2. B-spline curve evolution

3. T-spline level-set evolution

4. Overview Introduction Outline of our method B-spline curve evolution (2D) T-spline level-set evolution (2D & 3D) Refine the evolution result Experimental Results Conclusions

5. Introduction Geometric reconstruction from discrete point data sets has various applications: We consider two types of representations: Parametric curves Implicit curves/surfaces (level-sets) We provide a unified framework for both shape reconstruction from unorganized points and image segmentation.

6. Outline of our method We call the evolutionary curves/surfaces active curves/surfaces or active shape (to fit the target shape) Outline of our algorithm: Initialization (pre-compute the evolution speed function) Evolution (which generates time-dependant families of curves/surfaces, until some stopping criterion is satisfied) Refinement

7. Evolution equation We want to move the active curve/surface along its normal directions: - Points on the curve - Time variable - Unit normal vector - Evolution speed function -

8. Evolution speed function For image contour detection, we use a modified version of that proposed by Caselles et al. [Caselles1997]: For unorganized data points fitting, we use:

9. Parametric curve evolution B-spline curve representation B-spline curve evolution Evolution with normal velocity From evolution equation, we get Then we chooseby solving

10. Parametric curve evolution through discretization, we replace with Smoothness constraint

11. Solve the evolution equation To minimize the object function Parametric curve evolution by solving a sparse linear system depends on the noise level of the input data.

12. Level-sets evolution T-spline level sets Implicit T-spline curves and is the T-spline function, (cubic in our case)

14. Level-sets evolution Implicit T-spline surfaces and T-spline level sets evolution Evolution with normal velocity

15. Level-sets evolution The definition of level-sets implies Combine it with and, we get Then we chooseby solving

16. through discretization, we replace with Level-sets evolution Distance field constraint Why distance field constraint? To avoid the time-consuming re-initialization steps, which has to be frequently applied to restore the signed distance field property of the level-set function for most existing level-set evolutions.

17. Level-sets evolution

18. Since an ideal signed distance function satisfies, we propose Again, through discretization, we replace with where

19. Solve the evolution equation To minimize the object function Level-sets evolution by solving a sparse linear system Smoothness constraint

20. Influence of different weights

21. Refine the evolution result For the given data points, the evolution result is refined by solving a non-linear least squares problem, - Given data points - Closest point of, on the active curve/surface For the given image data, using detected edge points around the active curve as target data points.

22. Experimental results Parametric curve evolution (without noise)

23. Experimental results Parametric curve evolution (with noise)

24. Experimental results Implicit curve evolution (image segmentation)

25. Experimental results Implicit curve evolution (2D)

26. Experimental results Implicit curve evolution (3D)

27. Conclusions and future work Evolution process can be reduced to a (sparse) system of linear equations. Distance field constraints can avoid additional branches of the level-sets without using re-initialization steps. Future work Adaptive redistribution of control points during the evolution More intelligent and robust evolution speed function Other shape constraints (symmetries, convexity) Use dual evolution to combine advantages of both parametric and implicit representations

28. References V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours”, International Journal of Computer Vision, 22(1), 1997, pp. 61-79 B. Juettler and A. Felis, “Least-squares fitting of algebraic spline surfaces ”, Advances in Computational Mathematics, 17, 2002, pp. 135-152 W. Wang, H. Pottmann and Y. Liu, “Fitting B-spline curves to point clouds by squared distance minimization”, ACM Transactions on Graphics, to appear, 2005 T. W. Sederberg, J. Zheng, A. Bakenov and A. Nasri, “T-splines and T-NURCCS”, ACM Transactions on Graphics, 22(3), 2003, pp. 477- 484 J. Nocedal and S. J. Wright, “Numerical optimization”, Springer Verlag, 1999