Figure 1
Simplified flow chart showing the six steps that the ParChar code is based on. Before anything can be done, the inputs have to be set in step 1. As described in the text and shown in the flow chart, some of the steps can be skipped by setting the relevant input values to zero.
Figure 2
Four of the six processing steps using the Matlab example image Rice.png and the settings listed in Table 1. In Step 1, inputs are specified, in step 2, the original image is loaded (a), in step 3, the background is corrected and noise is removed (b), in step 4, thresholding is carried out and particles are identified (c) and in step 5, the particles are characterized, here exemplified by a histogram of sizes of the identified grains (d), plotted through step 6.
Table 1
Suggested code inputs to test if the code is working using either the rice grains example from Matlab or the Pcam example image supplied with the code.
| Input variable | Rice.png | Pcam example image |
|---|---|---|
| ImHeight | 20 | 15 |
| ImWidth | 20 | 22.5 |
| MeasDepth | 1 | 2 |
| SubDiff | 50 | 0 |
| Lvl | 0.6 | 0.1 |
| MaxInt | 0.5 | 0.5 |
| CalcPSD | 1 | 1 |
| PlotResults | 1 | 1 |
Figure 3
Example of the result of image processing using the ParChar code on an image from the Pcam of natural particles suspended in the water. The inputs for this example are shown in Table 1. Top left plot shows the original input image, bottom left plot shows the detected particles from the input images, top right plot shows the histogram count of particles in size bins and bottom right plot shows the frequency distribution of the sizes according to the size bins.