Name
Abstract | ||
|---|---|---|
In recent years, several devices allow to directly measure real vector fields, leading to a better understanding of fundamental phenomena such as fluid simulation or brainwater movement. This turns vector field visualization and analysis important tools for many applications in engineering and in medicine. However, real data is generally corrupted by noise, puzzling the understanding provided by those tools.Those tools thus need a denoising step as preprocessing, although usual denoising removes discontinuities, which are fundamental for vector field analysis. This paper proposes a novel method for vector field denoising based on random walks which preserve those discontinuities. It works in a meshless setting; it is fast, simple to implement, and shows a better performance than the traditional gaussian denoising technique. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/SIBGRAPI.2009.13 | SIBGRAPI |
Keywords | Field | DocType |
brain,data visualisation,image denoising,Gaussian denoising technique,brain water movement,discontinuity removal,fluid simulation,meshless setting,random walks,vector field denoising,vector field visualization,Denoising,Discrete Vector Field,Markov Chain,Random Walk | Noise reduction,Data visualization,Classification of discontinuities,Vector field,Random walk,Markov chain,Algorithm,Gaussian,Preprocessor,Artificial intelligence,Machine learning,Mathematics | Conference |
ISSN | Citations | PageRank |
1530-1834 | 1 | 0.36 |
References | Authors | |
14 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joao Paixao | 1 | 7 | 3.57 |
| Marcos Lage | 2 | 75 | 8.59 |
| Fabiano Petronetto | 3 | 103 | 8.52 |
| Alex Laier Bordignon | 4 | 30 | 2.76 |
| Sinésio Pesco | 5 | 44 | 7.20 |
| Geovan Tavares | 6 | 337 | 20.22 |
| Thomas Lewiner | 7 | 700 | 43.70 |
| Hélio Lopes | 8 | 248 | 21.84 |