@article{LI2019330,
title = "Multiple ellipse fitting of densely connected contours",
journal = "Information Sciences",
volume = "502",
pages = "330 - 345",
year = "2019",
issn = "0020-0255",
doi = "https://doi.org/10.1016/j.ins.2019.06.045",
url = "http://www.sciencedirect.com/science/article/pii/S0020025519305821",
author = "Hui Li",
keywords = "Multiple ellipse fitting, Sliding window, Anomaly detection, Cyclically ordered set, Pattern recognition",
abstract = "Multiple ellipse fitting is challenging and at the same time essential as it has a variety of applications in biology, chemistry, and nanotechnology. Accurate, effective, and reliable approach for the fitting problem has been always desirable. In this paper, we address a category of multiple ellipse fitting problem which fits densely connected contours. We propose a framework rather than design an algorithm for the problem. The framework streamlines five processes which include: sorting the contour points, doing ellipse fitting in sliding windows, detecting the context anomaly, performing clustering, and obtaining multiple ellipses through second ellipse fitting. The framework is evaluated in a real-world application of handprint identification and various synthetic datasets. Experimental results show that the framework can extract multiple ellipses from contours with satisfactory accuracy and efficiency."
}