Name
Abstract | ||
|---|---|---|
We present a novel approach to the multi-scale analysis of point-sampled manifolds of co-dimension 1. It is based on a variant of Moving Least Squares, whereby the evolution of a geometric descriptor at increasing scales is used to locate pertinent locations in scale-space, hence the name “Growing Least Squares”. Compared to existing scale-space analysis methods, our approach is the first to provide a continuous solution in space and scale dimensions, without requiring any parametrization, connectivity or uniform sampling. An important implication is that we identify multiple pertinent scales for any point on a manifold, a property that had not yet been demonstrated in the literature. In practice, our approach exhibits an improved robustness to change of input, and is easily implemented in a parallel fashion on the GPU. We compare our method to state-of-the-art scale-space analysis techniques and illustrate its practical relevance in a few application scenarios. © 2012 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1111/j.1467-8659.2012.03174.x | Comput. Graph. Forum |
Keywords | Field | DocType |
multiple pertinent scale,novel approach,multi-scale analysis,wiley periodicals,continuous solution,scale-space analysis method,pertinent location,geometric descriptor,state-of-the-art scale-space analysis technique,application scenario | Least squares,Parametrization,Computer science,Scale space,Theoretical computer science,Robustness (computer science),Moving least squares,Sampling (statistics),Manifold | Journal |
Volume | Issue | ISSN |
31 | 5 | 0167-7055 |
Citations | PageRank | References |
15 | 0.69 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Mellado | 1 | 168 | 12.23 |
| Gaël Guennebaud | 2 | 702 | 28.95 |
| Pascal Barla | 3 | 553 | 29.07 |
| Patrick Reuter | 4 | 21 | 1.80 |
| Christophe Schlick | 5 | 612 | 49.06 |