Abstract
The iterative closest point (ICP) algorithm is an efficient algorithm for robust rigid registration of 3D data. Results provided by the algorithm are highly dependent upon the step of finding corresponding pairs between the two sets of 3D data before registration. In this paper, a look up matrix is introduced in the point matching step to enhance the overall ICP performance. Convergence properties and robustness are evaluated in the presence of Gaussian and impulsive noise, and under different data set sizes. The new algorithm has been evaluated on 3D medical data. It has been applied successfully to register closed surfaces acquired using different medical imaging modalities.
| Original language | English |
|---|---|
| Pages (from-to) | 1523-1533 |
| Number of pages | 11 |
| Journal | Pattern Recognition Letters |
| Volume | 28 |
| Issue number | 12 |
| DOIs | |
| State | Published - 1 Sep 2007 |
Bibliographical note
Funding Information:This work was partially sponsored by a grant from the University of Sebha, Libya. The authors would like to thank particularly Dr. Fabienne Thérain and Dr. Long-Dang Nguyen, from the Nuclear Medicine and Cardiology Departments of the Regional Hospital Center of Orleans for providing the medical data.
Keywords
- ICP algorithm
- Medical data
- Point matching
- Surface registration
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
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