Support Vectors Learning for Vector Field Reconstruction
Support Vectors Learning for Vector Field Reconstruction
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DOI：
10.1109/sibgrapi.2009.20
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Marcos Lage;Rener Castro;Fabiano Petronetto;A. Bordignon;G. Tavares;T. Lewiner;H. Lopes
中科院分区：
文献类型：
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作者：
Marcos Lage;Rener Castro;Fabiano Petronetto;A. Bordignon;G. Tavares;T. Lewiner;H. Lopes
采样的矢量场通常表现为真实现象的测量。它们可以通过使用粒子图像测速采集设备来获得，或者作为物理模拟的结果，例如流体流动模拟等。本文提出通过机器学习来制定非结构化矢量场重建和近似。机器从样本中学习全局矢量场估计函数，可以在整个域的任意点进行评估。使用支持向量回归方法进行多尺度分析，所提出的方法通过有效的非线性优化为重建向量场提供全局解析表达式。对人工和真实数据的实验表明，所提出的技术具有统计上稳健的行为。
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.