Support vectors learning for vector field reconstruction

Support vectors learning for vector field reconstruction
Marcos Lage, Rener Castro, Fabiano Petronetto, Alex Bordignon, Geovan Tavares, Thomas Lewiner, Hélio Lopes

Abstract:

Sampled vector fields generally appear as measurements of real phenomena. They can be obtained by the use of a Particle Image Velocimetry acquisition device, or as the result of a physical simulation, such as a fluid flow simulation, among many examples. This paper proposes to formulate the unstructured vector field reconstruction and approximation through Machine-Learning. The machine learns from the samples a global vector field estimation function that could be evaluated at arbitrary points from the whole domain. Using an adaptation of the Support Vector Regression method for multi-scale analysis, the proposed method provides a global, analytical expression for the reconstructed vector field through an efficient non-linear optimization. Experiments on artificial and real data show a statistically robust behavior of the proposed technique.

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BibTeX:

@inproceedings{vectorfieldsvr_sibgrapi,
    author = {Marcos Lage and Rener Castro and Fabiano Petronetto and Alex Bordignon and Geovan Tavares and Thomas Lewiner and Hélio Lopes},
    title = {Support vectors learning for vector field reconstruction},
    year = {2009},
    month = {october},
    booktitle = {Sibgrapi 2009 (XXII Brazilian Symposium on Computer Graphics and Image Processing)},
    pages = {104--111},
    publisher = {IEEE},
    address = {Rio de Janeiro, RJ},
    doi = {10.1109/Sibgrapi.2009.20},
    url = {\url{http://thomas.lewiner.org/pdfs/vectorfieldsvr_sibgrapi.pdf}}
}