Name
Abstract | ||
|---|---|---|
This paper extends our prior work on multi-modal image registration based on the a priori knowledge of the joint intensity distribution that we expect to obtain, and Kullback-Leibler distance. This expected joint distribution can be estimated from pre-aligned training images. Experimental results show that, as compared with the Mutual Information and Approximate Maximum Likelihood based registration methods, the new method has longer capture range at different image resolutions, which can lead to a more robust image registration method. Moreover, with a simple interpolation algorithm based on non-grid point random sampling, the proposed method can avoid interpolation artifacts at the low resolution registration. Finally, it is experimentally demonstrated that our method is applicable to a variety of imaging modalities. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30135-6_73 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
maximum likelihood,low resolution,image registration,mutual information,random sampling,image resolution,a priori knowledge,kullback leibler distance | Computer vision,Joint probability distribution,Pattern recognition,Computer science,A priori and a posteriori,Interpolation,Sampling (statistics),Mutual information,Artificial intelligence,Image resolution,Kullback–Leibler divergence,Image registration | Conference |
Volume | ISSN | Citations |
3216 | 0302-9743 | 12 |
PageRank | References | Authors |
0.76 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Gan | 1 | 183 | 13.62 |
| Jue Wu | 2 | 145 | 13.05 |
| Albert C. S. Chung | 3 | 964 | 72.07 |
| Simon C. H. Yu | 4 | 83 | 7.86 |
| William M. Wells III | 5 | 5267 | 833.10 |