Name
Abstract | ||
|---|---|---|
Curve and surface-fitting are classic problems of approximation that find use in many fields, including computer vision. There are two broad approaches to the problem - interpolation, which seeks to fit points exactly, and regression, which seeks a rougher approximation which is more robust to noise. This survey looks at several techniques of both kinds, with a particular focus on applications in computer vision. We make use of an empirical first-level evaluation approach which scores the techniques on multiple features based on how important they are to users of the technique and developers. This provides a quick summary of the broad applicability of the technique to most situations, rather than a deep evaluation of the performance and accuracy of the technique obtained by running it on several datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1142/S0219467821500418 | INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS |
Keywords | DocType | Volume |
Curve fitting, comparative evaluation of methods | Journal | 21 |
Issue | ISSN | Citations |
04 | 0219-4678 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyle A. Brown | 1 | 0 | 3.04 |
| N. Bourbakis | 2 | 10 | 7.97 |