List of Tables

1. Optimization algorithms reviewed in section 2.2.2.
2. Common radial basis functions.
3. Properties of the normal distribution.
4. Properties of the multivariate normal distribution.
5. Parts of this thesis that deal with hyperparameters.
6. Advantages and disadvantages of the large and small margins.
7. RGE for the experiment of figure 5.23.
8. Summary of the notation used in chapter 7.
9. Statistics on the relative errors.
10. Statistics on the Gaussian curvatures.
11. Average 3D error.
12. Overview of our Feature-Driven Inverse Compositional Gauss-Newton registration.
13. Overview of our Learning-based registration.
14. Results for the first T-shirt sequence.
15. Results for the paper sequence.
16. Results for the rug sequence.
17. Results for the second T-shirt sequence.