Drop Distribution Determination in a Liquid-Liquid Dispersion by Image Processing

Abstract

This paper presents the implementation of an algorithm for automatic identification of drops with different sizes in monochromatic digitized frames of a liquid-liquid chemical process. These image frames were obtained at our Laboratory, using a nonintrusive process, with a digital video camera, a microscope, and an illumination setup from a dispersion of toluene in water within a transparent mixing vessel. In this implementation, we propose a two-phase approach, using a Hough transform that automatically identifies drops in images of the chemical process. This work is a promising starting point for the possibility of performing an automatic drop classification with good results. Our algorithm for the analysis and interpretation of digitized images will be used for the calculation of particle size and shape distributions for modelling liquid-liquid systems.

An automatic image analysis methodology for the measurement of droplet size distributions in liquid–liquid dispersion: round object detection

“…This finding was so successful that it has been implemented to OpenCV library. Recently (Bras et al, 2009) proposed a variation of the Hough transform dedicated for automatic measurement of DSD for LLD. The reduced voting procedure which used only two sections of the right circular cone was applied.…”

“…Research in this area indicates that the quality of acquired images have a big impact on the success of the automated detection of the objects of interest and their measurements (Yan, Sayad, & Balke, 2009). The influence of the image quality of automatic measurements precision and droplets detection success was tested and presented in (Bras, Gomes, Ribeiro, & Guimares, 2009) where the authors proposed algorithm for automatic image analysis for the measurement of DSD in an emulsion. To evaluate the robustness of this methodology against variations in image quality as objectively as possible they chose images with two different levels of quality.…”

“…Generally, all algorithms work well for designed systems but for only low coalescence -otherwise cannot give satisfying results in terms of droplets detection (Maaβ et al, 2012). However, promising results in (Alban et al, 2004;Bras et al, 2009) show a way forward. Alban et al (Alban et al, 2004) proposed an interesting combination of image processing algorithm for arc and circle center estimation and further a pattern matching algorithm for verification of results.…”

“…In this method the biggest problem was with small droplets especially when their contours are weakly visible. On the other hand, Bras et al (Bras et al, 2009) presented an image processing algorithm which allows extracting relevant information from an image and then used well known Hough transform for detection of circles (Cohen & Toussaint, 1977). This approach also has the biggest problem with the detection of small droplets but in both cases, it does not have a big impact on Sauter mean diameter because this diameter mainly depends on droplets whose sizes are medium to large.…”

Optical investigation of the fuel injector influence in a PFI spark ignition engine for two-wheel vehicles

“…To increase the sensitivity of the detection, a two-step convolution filter was applied before the threshold analysis. This filter highlights (by squared function) the pixels that have the edges (peak and valley) firstly in a kernel size equal to 7 x 7 and then equal to 3 x 3 [11,12]. After convolution the threshold filter based on interclass variance was applied (d).…”