Tomo-PIV measurement of flow around an arbitrarily moving body with surface reconstruction
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A three-dimensional surface of an arbitrarily moving body in a flow field was reconstructed using the DAISY descriptor and epipolar geometry constraints. The surface shape of a moving body was reconstructed with tomographic PIV flow measurement. Experimental images were captured using the tomographic PIV system, which consisted of four high-speed cameras and a laser. The originally captured images, which contained the shape of the arbitrary moving body and the tracer particles, were separated into the particle and surface images using a Gaussian smoothing filter. The weak contrast of the surface images was enhanced using a local histogram equalization method. The histogram-equalized surface images were used to reconstruct the surface shape of the moving body. The surface reconstruction method required a sufficiently detailed surface pattern to obtain the intensity gradient profile of the local descriptor. The separated particle images were used to reconstruct the particle volume intensity via tomographic reconstruction approaches. Voxels behind the reconstructed body surface were neglected during the tomographic reconstruction and velocity calculation. The three-dimensional three-component flow vectors were calculated based on the cross-correlation functions between the reconstructed particle volumes. Three-dimensional experiments that modeled the flows around a flapping flag, a rotating cylinder, and a flapping robot fish tail were conducted to validate the present technique.
- Arroyo MP, Hinsch KD (2008) Recent developments of PIV towards 3D measurements particle image velocimetry. Springer, Berlin, pp 127–154Google Scholar
- Barnhart DH, Adrian RJ, Menhart C, Papen GC (1995) Phase-conjugate holographic system for high-resolution particle image velocimetry through thick-walled curved windows. In: SPIE’s 1995 international symposium on optical science, engineering, and instrumentation. International Society for Optics and Photonics, pp 165–175Google Scholar
- Sonka M, Hlavac V, Boyle R (1999) Image processing, analysis, and machine vision. PWS Publishing, Pacific GroveGoogle Scholar
- Tola E, Lepetit V, Fua P (2008) A fast local descriptor for dense matching. In: IEEE conference on computer vision and pattern recognition, 2008 (CVPR 2008), IEEE, pp 1–8Google Scholar