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### 1.2 Detection of the particles from UV light imaging
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Though used most commonly in granular studies, discs are different from shapes of grains in reality. Hence there have been numerous studies on granular systems with different shapes other than discs, e.g., ellipses, polygons and star-shape-like particles. Here we choose star particles as an example to illustrate how to detect them from UV light imaging.
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Though used most commonly in granular studies, discs are different from shapes of grains in reality. Hence there have been numerous studies on granular systems with different shapes other than discs, e.g., ellipses, polygons and star-shape-like particles. Here we choose star particles as an example to illustrate how to detect them from UV light imaging, instead of white light imaging.
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With the particle positions and radii information, orientations can be found in the `U' image. Using the blue channel of the `U' image, e.g. Fig.~\ref{fig-diskori}(a), an adaptive threshold is applied locally to binarize the image so that the UV bars are 1 (bright) and the rest of the particle is 0 (dark), as shown in Fig.~\ref{fig-diskori}(b). Then a least squares fit with the minimized perpendicular offsets reveals a linear function between the x and y positions in each UV bar, shown in Fig.~\ref{fig-diskori}(c). The slope of the line gives the orientation associated with each particle.
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## 2 Tracking
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### 2.1 Direct tracking
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Once particle centers are detected, they can be tracked throughout the whole experiment. One efficient and accurate algorithm has been developed and implemented in Matlab by [John Crocker, David Grier and Eric Weeks](http://www.physics.emory.edu/faculty/weeks//idl/).
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Once particle centers are detected, they can be tracked throughout the whole experiment. One efficient and accurate algorithm has been developed and integrated into an IDL by [John Crocker, David Grier and Eric Weeks](http://www.physics.emory.edu/faculty/weeks//idl/). This method requires particle positions detected as an input. The main idea of this method is: given the positions for all detected particles at a step, and possible new positions detected at the next step, the algorithm considers all possible identifications of all the old positions with the new positions, and chooses that identification which results in the minimal total squared displacement. Missing particles for a certain interval of steps are allowed. An exemplary result of tracking photo-elastic particles under simple shear after a certain amount of shear strain is shown below.
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![Displacement](uploads/9ed05b12c40d76dbd03bc1b0ff848e69/Displacement.png)
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### 2.2 Measurement of the particle flow
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