Simultaneous measurement of particle position and displacement in depth by depth-from-defocus using time-series color images

M. Deguchi, S. Murata, Yohsuke Tanaka
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Abstract

Simultaneous measurement of particle position and displacement in depth by depth-from-defocus using time-series color images Miki DEGUCHI, Shigeru MURATA and Yohsuke TANAKA ABSTRACT This paper presents a method for simultaneous measurement of particle position and displacement in depth by depth-from-defocus using time series color images. This method enables us to obtain the particle information for 3D-PIV by using a single color camera. To improve the depth measurement accuracy, three differently blurred images are simultaneously captured as RGB color images and the depth information is estimated from the three images with a neural network considering in-plane position. In performance tests, we numerically and experimentally evaluate the measurement accuracy of particle position and displacement in depth. The results of depth position by neural network show that the RMS error decreases to 65% of that by a calibration line. The results of depth displacement from three color images show that the RMS error decreases to 24% of that from only one color image, corresponding to 9.5% for maximum displacement 2 mm.
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利用时间序列彩色图像进行离焦深度同时测量粒子位置和深度位移
Miki DEGUCHI, Shigeru MURATA和Yohsuke TANAKA提出了一种利用时间序列彩色图像同时测量粒子位置和深度位移的方法。该方法使我们能够使用单彩色相机获得3D-PIV的粒子信息。为了提高深度测量精度,同时捕获三幅不同模糊程度的图像作为RGB彩色图像,并利用考虑面内位置的神经网络对三幅图像进行深度估计。在性能测试中,通过数值和实验对颗粒位置和深度位移的测量精度进行了评价。结果表明,神经网络深度定位的均方根误差减小到标定线的65%。三幅彩色图像的深度位移结果表明,当最大位移为2 mm时,其均方根误差减小到单幅图像的24%,为9.5%。
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