A higher-order model for fluid motion estimation

Authors:
Wei Liu;Eraldo Ribeiro
Affiliations:
Computer Vision and Bio-Inspired Computing Laboratory, Florida Institute of Technology, Melbourne, FL;Computer Vision and Bio-Inspired Computing Laboratory, Florida Institute of Technology, Melbourne, FL
Venue:
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Year:
2011

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Abstract

Image-based fluid motion estimation is of interest to science and engineering. Flow-estimation methods often rely on physics-based or spline-based parametric models, as well as on smoothing regularizers. The calculation of physics models can be involved, and commonly used 2nd-order regularizers can be biased towards lower-order flow fields. In this paper, we propose a local parametric model based on a linear combination of complex-domain basis flows, and a resulting global field that is produced by blending together local models using partition-of-unity.We show that the global field can be regularized to an arbitrary order without bias towards specific flows. Additionally, the blending approach to fluid-motion estimation is more flexible than competing spline-based methods. We obtained promising results on both synthetic and real fluid data.