Learning Invariant Representations Of Planar Curves

Gautam Pai; Aaron Wetzler; Ron Kimmel

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arXiv · 1611.07807

Learning Invariant Representations Of Planar Curves

Gautam Pai·Aaron Wetzler·Ron Kimmel

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Abstract

We propose a metric learning framework for the construction of invariant geometric functions of planar curves for the Eucledian and Similarity group of transformations. We leverage on the representational power of convolutional neural networks to compute these geometric quantities. In comparison with axiomatic constructions, we show that the invariants approximated by the learning architectures have better numerical qualities such as robustness to noise, resiliency to sampling, as well as the ability to adapt to occlusion and partiality. Finally, we develop a novel multi-scale representation in a similarity metric learning paradigm.

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BibTeX RIS

Gautam Pai, Aaron Wetzler, Ron Kimmel. 2016-11-23. Learning Invariant Representations Of Planar Curves. https://arxiv.org/abs/1611.07807

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