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• W2804401370 abstract "Ellipse fitting is a highly researched and mature topic. Surprisingly, however, no existing method has thus far considered the data point eccentricity in its ellipse fitting procedure. Here, we introduce the concept of eccentricity of a data point, in analogy with the idea of ellipse eccentricity. We then show empirically that, irrespective of ellipse fitting method used, the root mean square error (RMSE) of a fit increases with the eccentricity of the data point set. The main contribution of the paper is based on the hypothesis that if the data point set were pre-processed to strategically add additional data points in regions of high eccentricity, then the quality of a fit could be improved. Conditional validity of this hypothesis is demonstrated mathematically using a model scenario. Based on this confirmation we propose an algorithm that pre-processes the data so that data points with high eccentricity are replicated. The improvement of ellipse fitting is then demonstrated empirically in real-world application of 3D reconstruction of a plant root system for phenotypic analysis. The degree of improvement for different underlying ellipse fitting methods as a function of data noise level is also analysed. We show that almost every method tested, irrespective of whether it minimizes algebraic error or geometric error, shows improvement in the fit following data augmentation using the proposed pre-processing algorithm." @default.
• W2804401370 created "2018-06-01" @default.
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• W2804401370 date "2018-05-15" @default.
• W2804401370 modified "2023-09-26" @default.
• W2804401370 title "Pre-processing by data augmentation for improved ellipse fitting" @default.
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• W2804401370 doi "https://doi.org/10.1371/journal.pone.0196902" @default.
• W2804401370 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5953444" @default.
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