Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel lower dimensional representation of a shape sequence. The proposed dimension reduction is invertible and computationally more efficient in comparison to other related works. Theoretically, the differential geometry tools such as moving frame and parallel transportation are successfully adapted into the dimension reduction problem of high dimensional curves. Intuitively, instead of searching for a global flat subspace for curve embedding, we deployed a sequence of local flat subspaces adaptive to the geometry of both of the curve and the manifold it lies on. In practice, the experimental results of the dimension reduction and reconstruction algorithms well illustrate the advantages of the proposed theoretical innovation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICCVW.2011.6130412 | 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCV WORKSHOPS) |
Keywords | DocType | Volume |
measurement,shape,dimension reduction,differential geometry,geometry,manifolds,pattern recognition,vectors,image reconstruction,mathematical model | Conference | abs/1108.0007 |
Issue | Citations | PageRank |
1 | 1 | 0.36 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng Yi | 1 | 97 | 5.89 |
| Hamid Krim | 2 | 520 | 59.69 |
| Larry K. Norris | 3 | 7 | 1.19 |