Abstract
This paper presents an improved Euclidean Ricci flow method for spherical parameterization. We subsequently invent a scale space processing built upon Ricci energy to extract robust surface features for accurate surface registration. Since our method is based on the proposed Euclidean Ricci flow, it inherits the properties of Ricci flow such as conformality, robustness and intrinsicalness, facilitating efficient and effective surface mapping. Compared with other surface registration methods using curvature or sulci pattern, our method demonstrates a significant improvement for surface registration. In addition, Ricci energy can capture local differences for surface analysis as shown in the experiments and applications.
| Original language | English |
|---|---|
| Pages (from-to) | 1107-1118 |
| Number of pages | 12 |
| Journal | Computer Vision and Image Understanding |
| Volume | 117 |
| Issue number | 9 |
| DOIs | |
| State | Published - 2013 |
Keywords
- Discrete Ricci flow
- Scale space
- Surface parameterization
- Surface registration
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