基于非线性滤波方法的PIV计算

卢宗庆; 廖庆敏; 裴继红

doi:10.3724/SP.J.1146.2009.00068

基于非线性滤波方法的PIV计算

doi: 10.3724/SP.J.1146.2009.00068

卢宗庆^①,,
廖庆敏^①,
裴继红^②

计量
- 文章访问数: 3150
- HTML全文浏览量: 80
- PDF下载量: 977
- 被引次数: 0
出版历程
- 收稿日期: 2009-01-16
- 修回日期: 2009-08-17
- 刊出日期: 2010-02-19

A PIV Approach Based on Nonlinear Filtering

摘要

摘要: 针对流体运动图像计算(也称为PIV)，为了获得可靠的运动矢量场、散度场和旋度场，该文提出了一种基于非线性滤波思想的PIV计算方法。新方法属于变分PIV方法，其在克服传统PIV方法不足的同时避开了经典变分方法中能量范函凸性和可微性的约束，将能量函数的最小化过程转变为非线性滤波过程。该文针对实际粒子图像序列与经典方法进行了实验和比较，结果证明新方法能够在有效抑制噪声的同时可以较好地保持在多流体运动的情况下运动矢量、散度和旋度场的细节信息。
- PIV; 光流; 扩散滤波; 变分
Abstract: For fluid motion image computation (PIV), a nonlinear filtering based PIV approach was proposed which can obtain reliable motion vector, div and curl fileds. As variational based PIV approaches the new approach is a nonlinear filtering process instead of an energy minimizing process, which can overcome the shortage of correlation based PIV approaches and avoid the restrictions of convexity and differentiability required by classical variational approaches. Experimental results from real particle image sequences demonstrated that the new method can help to suppress the computation noise and increase the reliability of fluid motion characteristic and structural descriptions.

HTML全文

参考文献(1)

Corpetti T, Heitz D, Arroyo G, Mmin E, and Santa-Cruz A. Fluid experimental flow estimation based on an optical-flow scheme. Experiments in Fluids, 2005, 40(1): 80-97.[2]Raffel M, willert C, and kompenhans J. Particle Image Velocimetry. A Practical Guide. Berlin: Springer, 2nd edition, 2001: 35-40.[3]Sakaino H. Fluid motion estimation method based on physical properties of waves. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008: 1-8.[4]Wernet M P. Fuzzy logic particle tracking velocimetry. Proceedings of the SPIE Conference on Optical Diagnostics in Fluid and Thermal Flow, SPIE, Bellingham, WA, 1993: 701-708.[5]Ruhnau P, Guetter C, Putze T, and Schnrr C. A variational approach for particle tracking velocimetry[J].Measurement Science and Technology.2005, 16(7):1449-1458[6]Chetverikov D, Nagy M, and Veresty J. Comparison of tracking techniques applied to digital PIV. Proc. International Conf. on Pattern Recognition, Barcelona, Spain, 2000, 4: 233-242.[7]Grant I. Particle image velocimetry: A review. Journal of Mechanical Engineering Science, 1997, 211(1): 55-76.[8]Gui L and Wereley S T. A correlation-based continuous window-shift technique to reduce the peak-locking effect in digital PIV image evaluation[J].Experiments in Fluids.2002, 32(4):506-517[9]Lourenco L M and Krothapalli A. True resolution PIV: a mesh-free second order accurate algorithm. The 10th International symposium on applications of laser techniques in fluid mechanics, Lisbon, Portugal, July 2000.[10]Corpetti T, Has P, Mmin E, and Papadakis N. Pressure image assimilation for atmospheric motion estimation. Tellus Series A: Dynamic Meteorology and Oceanography, 2008, 6(31): 160-178.[11]Horn B and Schunck B. Determining optical flow[J].Artificial Intelligence.1981, 17(1):185-203[12]Lu Z Q, Xie W X, and Pei J H. A robust optical flow computation. Journal of Electronics(China), 2007, 24(5): 635-641.[13]Heitz D, Has P, Mmin E, and Carlier J. Dynamic consistent correlation-variational approach for robust optical flow estimation[J].Experiments in Fluids.2008, 45(4):595-608[14]Ruhnau P, Stahl A, and Schnrr C. Variational estimation of experimental fluid flows with physics-based spatio-temporal regularization[J].Measurement Science and Technology.2007, 18(1):755-763[15]Barash D and Comaniciu D. A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift. Image and Video Computing, 2003, 22(1): 73-81.[16]Tomasi C and Manduchi R. Bilateral filtering for gray and color images. Proceedings of the IEEE International Conference on Computer Vision, Bombay, India, 1998: 839-846.

施引文献

资源附件(0)

访问统计