Name
Abstract | ||
|---|---|---|
In previous works on point registration based on finite mixture model, the correspondence probability is often determined by exploiting global relationship in the point set instead of considering the local point distribution. That results in a simplified registration model. In this paper a feature-dependant finite mixture model (FDMM) is proposed. In particular, an improved descriptor is introduced to describe the local feature of a point. Consequently, a priori density function is formulated for the mixture weights. The unknown parameters of FDMM are computed by maximizing a posteriori (MAP) estimation. Moreover, a bidirectional expectation-maximization (EM) process is introduced to update both point sets in contrast to traditional methods. The performance of our method is demonstrated and validated with carefully designed synthetic data and real data, showing that the proposed method can improve the robustness and accuracy as compared to the traditional registration techniques. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.patrec.2013.06.019 | Pattern Recognition Letters |
Keywords | Field | DocType |
robust non-rigid point registration,local point distribution,mixture weight,finite mixture model,feature-dependant finite mixture model,point set,registration model,point registration,local feature,traditional registration technique,expectation maximization | Point distribution model,Point set registration,Pattern recognition,Expectation–maximization algorithm,A priori and a posteriori,Robustness (computer science),Synthetic data,Artificial intelligence,Probability density function,Mixture model,Mathematics | Journal |
Volume | Issue | ISSN |
34 | 13 | 0167-8655 |
Citations | PageRank | References |
4 | 0.40 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Sang | 1 | 4 | 0.74 |
| Jian-Zhou Zhang | 2 | 22 | 5.38 |
| Zeyun Yu | 3 | 280 | 27.13 |