Learning Invariant Representations Of Planar Curves

Gautam Pai, Aaron Wetzler, Ron Kimmel

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.

Conflicts: cs.technion.ac.il

Keywords: Computer vision, Deep learning, Supervised Learning, Applications