Sridhar, H and Harish Kumar, JR and Jois, S and Seelamantula, CS (2019) An unconstrained ellipse fitting technique and application to optic cup segmentation. In: 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings, 26 November 2018 through 29 November 2018, United States, pp. 514-518.
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Abstract
We present a novel method for fitting an ellipse to scattered data based on least-squares minimization. The new technique has several advantages over the standard ellipse fitting techniques. For one, it is constraint-free and computationally inexpensive thus making it easy to implement. Also, despite the absence of constraints, execution of the model always results in an ellipse fit. Additionally, the model results in a singular solution for a given set of datapoints. The proposed model is compared with standard techniques and shown to have the ability to fit an accurate ellipse even when other methods either fail to be ellipse-specific or take up excessive computation time for execution. An application to the problem of segmentation of the optic cup in retinal fundus images, is also presented. Experimental validation and performance comparisons show that the proposed technique is competitive with the state-of-the-art methods. © 2018 IEEE.
Item Type: | Conference Paper |
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Publication: | 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Additional Information: | The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc. |
Keywords: | Image segmentation; Least squares approximations; Ophthalmology, Ellipse fit; Experimental validations; Least squares minimization; Optic cups; Performance comparison; Retinal fundus images; Singular solutions; State-of-the-art methods, Geometry |
Department/Centre: | Division of Electrical Sciences > Electrical Engineering |
Date Deposited: | 06 May 2019 12:48 |
Last Modified: | 26 Aug 2022 04:10 |
URI: | https://eprints.iisc.ac.in/id/eprint/62155 |
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