4 citations · 10 across the 10 of their papers we have counts for
7 papers · 1 filter
Learning Differential Invariants of Planar Curves
Roy Velich, Ron Kimmel
We propose a learning paradigm for the numerical approximation of differential invariants of planar curves. Deep neural-networks' (DNNs) universal approximation properties are util…
Partial Shape Similarity via Alignment of Multi-Metric Hamiltonian Spectra
David Bensaïd, Amit Bracha, Ron Kimmel
Evaluating the similarity of non-rigid shapes with significant partiality is a fundamental task in numerous computer vision applications. Here, we propose a novel axiomatic method…
Depth Refinement for Improved Stereo Reconstruction
Amit Bracha, Noam Rotstein, David Bensaïd +2
Depth estimation is a cornerstone of a vast number of applications requiring 3D assessment of the environment, such as robotics, augmented reality, and autonomous driving to name a…
Learning Invariant Representations Of Planar Curves
Gautam Pai, Aaron Wetzler, Ron Kimmel
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…
3D Face Reconstruction by Learning from Synthetic Data
Elad Richardson, Matan Sela, Ron Kimmel
Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications. Here, we introduce a learning-b…
On the optimality of shape and data representation in the spectral domain
Yonathan Aflalo, Haim Brezis, Ron Kimmel
A proof of the optimality of the eigenfunctions of the Laplace-Beltrami operator (LBO) in representing smooth functions on surfaces is provided and adapted to the field of applied…