A fully-automated image processing technique to improve measurement of suspended particles and flocs by removing out-of-focus objects

Authors:
Ali Keyvani;Kyle Strom
Affiliations:
Civil and Environmental Engineering, University of Houston, Houston, TX, USA;Civil and Environmental Engineering, University of Houston, Houston, TX, USA
Venue:
Computers & Geosciences
Year:
2013

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Abstract

A fully-automated image processing script was developed to analyze large datasets of imaged flocs in dilute turbulent suspensions of mud. In the procedure, out-of-focus flocs are automatically removed from the dataset to attain a more precise floc size distribution. This automated technique was tested against visual inspection of images to ensure that the procedure was only selecting in-focus flocs for inclusion in the size measurements, and the resulting measured sizes were compared to floc measured through manual image processing of the same data. The results show that the automated method is able to accurately measure the floc size distribution by correctly sizing in-focus flocs and removing out-of-focus flocs. The processing procedures were developed with sizing of suspended mud flocs in mind, but the process is general and can be applied for other applications. We show the ability of the method to handle large numbers of images (over 15,000 at a time) by tracking the change in floc size population with time at 1-min intervals over the course of a 160min floc growth experiment.