Abstract
In this article, we explore the effectiveness of using neural networks to generate skeleton-based representations of shapes. Deep-learning approaches have proven very efficient to extract meaningful information from images. Our goal is to learn a mapping from a binary image of a 2D shape to a parametric Bézier curve representation of the medial axis of the shape using a convolutional neural network. We determine the most salient curves in the Blum medial axis and then train a CNN to produce one, two, and five curve medial representations. Using a Bézier curve representation of the medial axis reduces the number of parameters, since we express medial curves and their radii as degree-five Bézier curves, and learn only the associated control points rather than the full point set of the medial axis.
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Notes
- 1.
MPEG-7 dataset: http://www.dabi.temple.edu/$sim$shape/MPEG7/dataset.html.
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Fondevilla, A., Morin, G., Leonard, K. (2021). Towards Learning Geometric Shape Parts. In: Demir, I., Lou, Y., Wang, X., Welker, K. (eds) Advances in Data Science. Association for Women in Mathematics Series, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-030-79891-8_5
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