Name
Abstract | ||
|---|---|---|
Inspired by retinex theory, we propose a novel method for selecting key points from a depth map of a 3D freeform shape; we also use these key points as a basis for shape registration. To find key points, first, depths are transformed using the Hotelling method and normalized to reduce their dependence on a particular viewpoint. Adaptive smoothing is then applied using weights which decrease with spatial gradient and local inhomogeneity; this preserves local features such as edges and corners while ensuring smoothed depths are not reduced. Key points are those with locally maximal depths, faithfully capturing shape. We show how such key points can be used in an efficient registration process, using two state-of-the-art iterative closest point variants. A comparative study with leading alternatives, using real range images, shows that our approach provides informative, expressive, and repeatable points leading to the most accurate registration results. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.patcog.2013.12.015 | Pattern Recognition |
Keywords | Field | DocType |
novel method,efficient registration process,local inhomogeneity,hotelling method,freeform shape,key point,point selection,local feature,shape registration,adaptive smoothing,accurate registration result,retinex | Computer vision,Color constancy,Normalization (statistics),Pattern recognition,Smoothing,Artificial intelligence,Depth map,Mathematics,Iterative closest point,Freeform (shape) | Journal |
Volume | Issue | ISSN |
47 | 6 | 0031-3203 |
Citations | PageRank | References |
5 | 0.44 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yonghuai Liu | 1 | 675 | 61.65 |
| Ralph R. Martin | 2 | 3279 | 240.42 |
| Luigi De Dominicis | 3 | 7 | 1.16 |
| Baihua Li | 4 | 176 | 21.71 |