Total Variation and Euler's Elastica for Supervised Learning

Supervised learning via Euler's Elastica models

This paper investigates the Euler’s elastica (EE) model for high-dimensional supervised learning problems in a function approximation framework. In 1744 Euler introduced the elastica energy for a 2D curve on modeling torsion-free thin elastic rods. Together with its degenerate form of total variation (TV), Euler’s elastica has been successfully applied to low-dimensional data processing such as...

A Convex, Lower Semicontinuous Approximation of Euler's Elastica Energy

We propose a convex, lower semi-continuous, coercive approximation of Euler’s elastica energy for images, which is thus very well-suited as a regularizer in image processing. The approximation is not quite the convex relaxation, and we discuss its close relation to the exact convex relaxation as well as the difficulties associated with computing the latter. Interestingly, the convex relaxation ...

Euler's Elastica and Curvature Based Inpaintings

From the elastica compass to the elastica catapult: an essay on the mechanics of soft robot arm

An elastic rod is clamped at one end and has a dead load attached to the other (free) end. The rod is then slowly rotated using the clamp. When the load is smaller than the buckling value, the rod describes a continuous set of quasi-static forms and its end traces a (smooth, convex and simple) closed curve, which would be a circle if the rod were rigid. The closed curve is analytically determin...

Image Segmentation Using Euler's Elastica as the Regularization

The active contour segmentation model of Chan and Vese has been widely used and generalized in different contexts in the literature. One possible modification is to employ Euler’s elastica as the regularization of active contour. In this paper, we study the new effects of this modification and validate them numerically using the augmented Lagrangian method.