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Detect and track particles
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Most experiments study a model 2 dimensional (2D) granular system composed of circular discs as grains. While in cases of studying particle shape effect, discs are replaced by other shapes like ellipses, polygons and crosses \cite{}. With a digital camera, the first and one of the most important information that can be obtained is particle positions and particle orientations. The latter are informative even for circular discs due to existence of inter-particle friction in most experiments. Particle detection and tracking alone provide rich information such as particle configuration and flow field, and are necessary for further measurements like force-bearing contact detection and contact force calculation. In this session, a detailed description for detecting particle positions and orientations and tracking particles in high resolution images will be summarized below. In addition, in images with resolution not high enough to precisely track particles, another method (PIV) will be introduced.
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Most experiments study a model 2 dimensional (2D) granular system composed of circular discs as grains. While in cases of studying particle shape effect, discs are replaced by other shapes like ellipses, polygons and crosses \cite{}. With a digital camera, the first and one of the most important information that can be obtained is particle positions. Particle detection and tracking alone provide rich information such as particle configuration and flow field, and are necessary for further measurements like force-bearing contact detection and contact force calculation. In this session, a detailed description for detecting particle positions and orientations and tracking particles in high resolution images will be summarized below. In addition, in images with resolution not high enough to precisely detect particle positions, another method (PIV) will be introduced.
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## 1 Position detection and tracking
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### 1.1 Detection of the particles from white light imaging
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* From the blue channel of the white light image, e.g. Fig.~\ref{fig-diskpos}(a), a mapping of the intensities to new values by suppressing low and high intensities is employed to increase the contrast of the image.
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* Then a low-pass filter is applied to the Fourier transform of the image to remove straight lines caused by the edges of slats, especially for the edges passing through particles in the image. Fig.~\ref{fig-diskpos}(b) shows the result after enhancing the contrast and removing lines inside of the particles.
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* Finally a circular Hough transform is applied to the gray scale image in Fig.~\ref{fig-diskpos}(b) to find centers and diameters of the discs \cite{peng07jcise}. The transform is an algorithm computing the curvature at each pixel point based on the image gray scale gradient. The curvature will automatically give the center and radius. Then a voting process rules out fake circles and combines circles with close enough centers and similar radii. The final result of particle identification is shown in Fig.~\ref{fig-diskpos}(c), where blue circles indicate particle centers and radii given by the algorithm with errors smaller than $0.02$ times the particle diameter.
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#### 1.1.1 Thresholding
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#### 1.1.2 Circular Hough transform
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#### 1.1.3 Convolution method
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... | ... | @@ -20,11 +27,7 @@ With the particle positions and radii information, orientations can be found in |
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### 2.1 Direct tracking
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In order to detect particle orientation, we usually put an artificial UV ink bar on the surface of particles.
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* Thresholding
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* Least Squares Fitting for Perpendicular Offsets
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Erick track.
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### 2.2 Measurement of the particle flow
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... | ... | |