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Single-camera three-dimensional tracking of natural particulate and zooplankton

机译:单摄像型三维跟踪自然颗粒和浮游动物

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摘要

We develop and characterize an image processing algorithm to adapt single-camera defocusing digital particle image velocimetry (DDPIV) for three-dimensional (3D) particle tracking velocimetry (PTV) of natural particulates, such as those present in the ocean. The conventional DDPIV technique is extended to facilitate tracking of non-uniform, non-spherical particles within a volume depth an order of magnitude larger than current single-camera applications (i.e. 10cm x 10cm x 24cm depth) by a dynamic template matching method. This 2D cross-correlation method does not rely on precise determination of the centroid of the tracked objects. To accommodate the broad range of particle number densities found in natural marine environments, the performance of the measurement technique at higher particle densities has been improved by utilizing the time-history of tracked objects to inform 3D reconstruction. The developed processing algorithms were analyzed using synthetically generated images of flow induced by Hill's spherical vortex, and the capabilities of the measurement technique were demonstrated empirically through volumetric reconstructions of the 3D trajectories of particles and highly non-spherical, 5 mm zooplankton.
机译:我们开发和表征图像处理算法,以适应自然颗粒的三维(3D)粒子跟踪速度(PTV)的单摄像头散焦数字粒子图像速度(DDPIV),例如海洋中存在的那些。通过动态模板匹配方法延伸,延长传统的DDPIV技术以促进在体积深度内的卷深度的不均匀非球形颗粒的跟踪。该2D交叉相关方法不依赖于对跟踪物体的质心的精确确定。为了适应天然海洋环境中发现的广泛粒子数密度,通过利用跟踪物体的时历史来告知3D重建,通过了更高粒子密度下提高了测量技术在更高的粒子密度下的性能。使用由山球形涡流引起的综合生成的流动图像分析开发的处理算法,并且通过粒子的3D轨迹的体积重建和高度非球形,5mm浮游车的体积重建来证明测量技术的能力。

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