title: Learning Invariant Representations Of Planar Curves 
abstract: We propose a metric learning framework for the construction of invariant geometric
  functions of planar curves for the Euclidean 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.
authors: Gautam Pai Aaron Wetzler Ron Kimmel
hIndex: Ron Kimmel 69