Robust Real-time Ellipse Fitting Based on Lagrange Programming Neural Network and Locally Competitive Algorithm

TL;DR

This paper introduces a robust, real-time ellipse fitting method that effectively handles outliers by combining Lagrange programming neural networks with locally competitive algorithms, outperforming existing methods.

Contribution

The paper presents a novel real-time ellipse fitting approach using LPNN and LCA to robustly handle outliers in edge-detected scattering points.

Findings

Outperforms several state-of-the-art algorithms in robustness and accuracy.

Effectively handles outliers using l1 and l0 norm-based optimization.

Achieves real-time performance in ellipse fitting tasks.

Abstract

Given a set of 2-dimensional (2-D) scattering points, which are usually obtained from the edge detection process, the aim of ellipse fitting is to construct an elliptic equation that best fits the collected observations. However, some of the scattering points may contain outliers due to imperfect edge detection. To address this issue, we devise a robust real-time ellipse fitting approach based on two kinds of analog neural network, Lagrange programming neural network (LPNN) and locally competitive algorithm (LCA). First, to alleviate the influence of these outliers, the fitting task is formulated as a nonsmooth constrained optimization problem in which the objective function is either an l1-norm or l0-norm term. It is because compared with the l2-norm in some traditional ellipse fitting models, the lp-norm with p<2 is less sensitive to outliers. Then, to calculate a real-time solution of this optimization problem, LPNN is applied. As the LPNN model cannot handle the non-differentiable term in its objective, the concept of LCA is introduced and combined with the LPNN framework. Simulation and experimental results show that the proposed ellipse fitting approach is superior to several state-of-the-art algorithms.