Computational geometry: an introduction
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Pattern Recognition Letters
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Pattern Recognition Letters
Cubist Style Rendering from Photographs
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IMITATION LEARNING AND ANCHORING THROUGH CONCEPTUAL SPACES
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Advanced Engineering Informatics
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In the literature, methods for fitting superellipses to data tend to be computationally expensive due to the nonlinear nature of the problem. This paper describes and tests several fitting techniques which provide different trade-offs between efficiency and accuracy. In addition, we describe various alternative error of fit (EOF) measures that can be applied by most superellipse fitting methods.