We propose a learning paradigm for numerical approximation of differential invariants of
planar curves. Deep neural-networks'(DNNs) universal approximation properties are utilized …

Isometric feature mapping is an established time-honored algorithm in manifold learning
and non-linear dimensionality reduction. Its prominence can be attributed to the output of a …

We propose a learning paradigm for the numerical approximation of differential invariants of
planar curves. Deep neural-networks'(DNNs) universal approximation properties are utilized …

Extracting meaningful representations from geometric data has prime importance in the
areas of computer vision, computer graphics, and image processing. Classical approaches …

In this work, we used deep neural networks (DNNs) to solve a fundamental problem in
differential geometry. One can find many closed-form expressions for calculating curvature …

In this work, we used a deep learning (DL) model to solve a fundamental problem in
differential geometry. One can find many closed-form expressions for calculating curvature …

In this work, we used a deep learning (DL) model to solve a fundamental problem in
differential geometry. One can find many closed-form expressions for calculating curvature …

The calculation of curve length is one of the most major components in many modern and
classical problems. For example, a handwritten signature involves the computation of the …