Name
Abstract | ||
|---|---|---|
The classical affine iterative closest point (ICP) algorithm is fast and accurate for affine registration between two point sets, but it is easy to fall into a local minimum. As an extension of the classical affine registration algorithm, this paper first proposes an affine ICP algorithm based on control point guided, and then applies this new method to establish a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the point set non-rigid registration from coarse to fine. In each iteration, the sub data point sets and sub model point sets are divided, meanwhile, the shape control points of each sub point set are updated. Then we use the control point guided affine ICP algorithm to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. Experimental results demonstrate that the accuracy and convergence of our algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11063-017-9760-x | Neural Processing Letters |
Keywords | Field | DocType |
Iterative closest point,Shape control point,Affine registration,Non-rigid registration,Hierarchical iteration | Affine transformation,Convergence (routing),Control point,Shape control,Pattern recognition,Iterative method,Algorithm,Artificial intelligence,Point set,Mathematics,Iterative closest point | Journal |
Volume | Issue | ISSN |
48 | 3 | 1370-4621 |
Citations | PageRank | References |
1 | 0.35 | 34 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Xiong | 1 | 44 | 3.44 |
| Liyang Wu | 2 | 1 | 0.35 |
| Wenting Cui | 3 | 1 | 0.69 |
| Sirui Zhang | 4 | 6 | 1.48 |
| Guanglin Xu | 5 | 26 | 9.95 |
| Huaizhong Hu | 6 | 8 | 1.16 |