Abstract
Given a sparse set of feature matches, we want to compute an interpolated dense displacement map. The application may be stereo disparity computation, flow
computation, or non-rigid medical registration. Also estimation of missing image data, may be phrased in this framework. Since the features often are very
sparse, the interpolation model becomes crucial. We show that a maximum likelihood estimation based on the covariance properties (Kriging) show properties
more expedient than methods such as Gaussian interpolation or Tikhonov regularizations, also including scale-selection. The computational complexities
are identical. We apply the maximum likelihood interpolation to growth analysis of the mandibular bone. Here, the features used are the crest-lines of the object
surface.
computation, or non-rigid medical registration. Also estimation of missing image data, may be phrased in this framework. Since the features often are very
sparse, the interpolation model becomes crucial. We show that a maximum likelihood estimation based on the covariance properties (Kriging) show properties
more expedient than methods such as Gaussian interpolation or Tikhonov regularizations, also including scale-selection. The computational complexities
are identical. We apply the maximum likelihood interpolation to growth analysis of the mandibular bone. Here, the features used are the crest-lines of the object
surface.
| Original language | English |
|---|---|
| Title of host publication | IEEE 1998 International Conference on Image Processing (ICIP 98), Chigaco, USA |
| Number of pages | 5 |
| Publisher | IEEE Computer Society Press |
| Publication date | 1998 |
| Publication status | Published - 1998 |
| Event | IEEE 1998 International Conference on Image Processing - Chicago, United States Duration: 7 Oct 1998 → 7 Oct 1998 |
Conference
| Conference | IEEE 1998 International Conference on Image Processing |
|---|---|
| Country/Territory | United States |
| City | Chicago |
| Period | 07/10/1998 → 07/10/1998 |