A PDE Approach to Data-driven Sub-Riemannian Geodesics in SE(2)

TL;DR

This paper introduces a new wavefront propagation algorithm for computing sub-Riemannian geodesics in SE(2), leveraging Hamilton-Jacobi-Bellman equations and Pontryagin's Maximum Principle, with applications in image analysis.

Contribution

It presents a flexible, accurate method for computing SR-geodesics in SE(2) that accounts for external image-based costs, improving structure tracking in images.

Findings

Accurate SR-spheres and geodesics compared to exact solutions

Global minimizers obtained for uniform cost case

Effective tracking of elongated structures in images

Abstract

We present a new flexible wavefront propagation algorithm for the boundary value problem for sub-Riemannian (SR) geodesics in the roto-translation group $SE(2) = \mathbb{R}^2 \rtimes S^1$ with a metric tensor depending on a smooth external cost $\mathcal{C}:SE(2) \to [\delta,1]$, $\delta>0$, computed from image data. The method consists of a first step where a SR-distance map is computed as a viscosity solution of a Hamilton-Jacobi-Bellman (HJB) system derived via Pontryagin's Maximum Principle (PMP). Subsequent backward integration, again relying on PMP, gives the SR-geodesics. For $\mathcal{C}=1$ we show that our method produces the global minimizers. Comparison with exact solutions shows a remarkable accuracy of the SR-spheres and the SR-geodesics. We present numerical computations of Maxwell points and cusp points, which we again verify for the uniform cost case $\mathcal{C}=1$. Regarding image analysis applications, tracking of elongated structures in retinal and synthetic images show that our line tracking generically deals with crossings. We show the benefits of including the sub-Riemannian geometry.