Name
Abstract | ||
|---|---|---|
For various 3D shape analysis tasks, the Laplace-Beltrami(LB) embedding has become increasingly popular as it enables the efficient comparison of shapes based on intrinsic geometry. One fundamental difficulty in using the LB embedding, however, is the ambiguity in the eigen-system, and it is conventionally only handled in a heuristic way. In this work, we propose a novel and intrinsic metric, the spectral l2-distance, to overcome this difficulty. We prove mathematically that this new distance satisfies the conditions of a rigorous metric. Using the resulting optimal embedding determined by the spectral l2-distance, we can perform both local and global shape analysis intrinsically in the embedding space. We demonstrate this by developing a template matching approach in the optimal embedding space to solve the challenging problem of identifying major sulci on vervet cortical surfaces. In our experiments, we validate the robustness of our method by the successful identification of major sulcal lines on a large data set of 698 cortical surfaces and illustrate its potential in brain mapping studies. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CVPR.2010.5540023 | CVPR |
Keywords | Field | DocType |
laplace-beltrami embedding,laplace equations,image matching,shape recognition,spectral l2-distance,optimal embedding space,global shape analysis,intrinsic 3d shape analysis,template matching approach,intrinsic geometry,optimal embedding,intrinsic metric,metric induced optimal embedding,eigen system,eigenvalues and eigenfunctions,geometry,shape,satisfiability,brain mapping,embedded computing,image analysis,measurement,biomedical imaging,manifolds,template matching,surface reconstruction,nervous system,shape analysis | Template matching,Heuristic,Embedding,Pattern recognition,Computer science,Intrinsic metric,Robustness (computer science),Artificial intelligence,Ambiguity,Manifold,Shape analysis (digital geometry) | Conference |
Volume | Issue | ISSN |
2010 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-4244-6984-0 | 15 | 0.72 |
References | Authors | |
15 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rongjie Lai | 1 | 239 | 19.84 |
| Yonggang Shi | 2 | 598 | 54.47 |
| Kevin Scheibel | 3 | 15 | 0.72 |
| Scott Fears | 4 | 15 | 0.72 |
| Roger P. Woods | 5 | 602 | 115.30 |
| Arthur W. Toga | 6 | 3128 | 261.46 |
| Tony F. Chan | 7 | 8733 | 659.77 |