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- W2553556021 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." @default.
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- W2553556021 date "2016-11-04" @default.
- W2553556021 modified "2023-09-26" @default.
- W2553556021 title "Learning Invariant Representations Of Planar Curves" @default.
- W2553556021 hasPublicationYear "2016" @default.
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