AUTOMATED DATA ANALYSIS FOR CONSECUTIVE IMAGES FROM DROPLET COMBUSTION EXPERIMENTS

Christopher Lee Dembia, Yu Cheng Liu, C. Thomas Avedisian

Abstract

A simple automated image analysis algorithm has been developed that processes consecutive images from high speed, high resolution digital images of burning fuel droplets. The droplets burn under conditions that promote spherical symmetry. The algorithm performs the tasks of edge detection of the droplet’s boundary using a grayscale intensity threshold, and shape fitting either a circle or ellipse to the droplet’s boundary. The results are compared to manual measurements of droplet diameters done with commercial software. Results show that it is possible to automate data analysis for consecutive droplet burning images even in the presence of a significant amount of noise from soot formation. An adaptive grayscale intensity threshold provides the ability to extract droplet diameters for the wide range of noise encountered. In instances where soot blocks portions of the droplet, the algorithm manages to provide accurate measurements if a circle fit is used instead of an ellipse fit, as an ellipse can be too accommodating to the disturbance.


Keywords
conic fitting; droplet combustion; edge detection; ellipse fitting; image analysis; weighted least squares

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Supplementary files
1. DROPLETD2.m    Download (169KB)


DOI: 10.5566/ias.v31.p137-148

Image Analysis & Stereology
EISSN 1854-5165 (Electronic version)
ISSN 1580-3139 (Printed version)