CHAPTER 3: METHODOLOGY
3.3. Data Processing
3.3.1. Processing of jar test observations
The techniques used to process the images obtained from the aggregation and deflocculation tests have been summarized in key points below:
1. Captured images from the jar test were transferred from the camera to a computer. The images were in RAW format. The Nikon ViewNX2 software package was used to convert the images into TIFF format for further processing. The imported images were then catalogued and stored.
2. An image was imported into MATLAB where it was converted into a binary image at a particular threshold. The threshold was selected by trial and error to produce a binary image where the sizes of the flocs in the original image are best preserved. The threshold was generally applicable to all images taken at a particular concentration and shear rate.
3. The images were exposed to more light near the top of the jar. This was unavoidable and resulted in an intensity gradient across the binary image. The top of the image was white while the bottom was black. Depending on the value of the threshold, there was a band across the central region of the image where the floc shapes and sizes were preserved. This area was cropped and saved as a new image for further processing. The original image was approximately 24x15 mm in size. The cropped image was typically approximately 15x4mm2 in size. (refer to plates 3-12 and 3-13 below) Two to three images were processed to improve the statistical reliability of the results.
4. The cropped binary images were processed using the same Matlab script. The script measures the area, equivalent diameter and major axis length of all objects in the binary image. A description of the digital imaging parameters is given in section 3.3.3. The script calculated the 25th, 50th, 75thand 90th percentiles of the floc areas measured. It also output the mean and maximum floc sizes. All particles present in the binary imageless than 20μm were filtered out.
The percentiles were recalculated based on the population above 20μm. In order to improve the accuracy of the processed data it was necessary to apply a 20μm filter. This was done to remove noise associated with the thresh-holding and creation of binary image. This is discussed in greater detail in chapter 4.
5. The output from the image processing script was entered into an excel spread sheet. An html file containing all the input and output data, as well as the Matlab script was produced using Matlab. Tabulated output is presented in the appendices. The html output file has been included in appendix E.
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Development of the image processing script in Matlab
The script used to process the images obtained was developed using the Matlab image processing toolbox. The html file in appendix E includes the script. The script was developed by the student using introductory books on Matlab for assistance. It is not a complex script. The processing of images was however time consuming.
Images are created and recognised as true-color images, which are the superposition of three indexed images: a blue, green and red image. Each indexed image is a matrix of numbers associated with a color map. The superposition of the three matrices produces a full colour image. Once the images were imported into matlab they converted into binary images (as mentioned above). A binary image is a matrix of 1’s and 0’s, where every colourscale below a stipulated threshold becomes a 1, and every colourscale above becomes a 0. The Matlab image processing toolbox has built in functions with which to analyse binary images. These are known as the regionprops functions. These were appropriately selected by the student to output parameters such as: number of objects (groups of white pixels), the equivalent diameter of all objects (in no. of pixels), the major axis length of all objects etc. This output is given in the form of matrices. In this form, the data may be easily filtered, plotted in the form of histograms, and statistically analysed. Refere to appendix E for an example of the image processing script and its output. Graphs were created either in Matlab, or by exporting output matrices and statistical parameters into Excel and plotting graphs there.
Camera calibration to identify floc size:
In studies of this nature digital imaging systems are often calibrated using different size classes of particles. A material of known size class is added to solution (e.g. polystyrene beads of 50µm diameter). Tests are run for a few size classes. The results are analysed and compared to the known particle sizes. This indicates the accuracy of the digital imaging system. Regrettibly this form of testing could not be performed in this study. It was not possible to obtain particles such as polystyrene beads of the size range required for calibration. Such materials are not commercially available in South Africa. The study was performed without this form of calibration. During the testing a scale ruler was used to confirm the scale of the images. It was assumed that all shapes (larger than 20µm in effective diameter) present in the processed binary images were sediment flocs. The filtration of shapes less than 20µm has been discussed in detail in chapter 4, but also alluded to above. A comparison between the image characteristics of clay, silt and sand solutions was not considered necessary for this study.
Note that each image processed was individually scrutinized before the results were accepted.
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Plate 3-12: A: An Image captured during an aggregation test. B: Binary image of A.
Plate 3-13: C: Crop of binary image. D: Image C altered for processing. The 20μm filter removed the ‘dust’ or noise from the image.
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