New Methods for Detecting Concentric Objects With High Accuracy

TL;DR

This paper introduces new statistical methods for accurately detecting concentric geometric objects, such as ellipses, in digitized data, improving robustness and initial estimation for iterative fitting in various applications.

Contribution

The paper develops a general statistical framework, derives variances and biases of existing methods, and proposes new estimators that outperform existing non-iterative approaches.

Findings

New estimators provide more reliable initial guesses.

Proposed methods outperform existing non-iterative techniques.

Methods are robust against large noise levels.

Abstract

Fitting concentric geometric objects to digitized data is an important problem in many areas such as iris detection, autonomous navigation, and industrial robotics operations. There are two common approaches to fitting geometric shapes to data: the geometric (iterative) approach and algebraic (non-iterative) approach. The geometric approach is a nonlinear iterative method that minimizes the sum of the squares of Euclidean distances of the observed points to the ellipses and regarded as the most accurate method, but it needs a good initial guess to improve the convergence rate. The algebraic approach is based on minimizing the algebraic distances with some constraints imposed on parametric space. Each algebraic method depends on the imposed constraint, and it can be solved with the aid of the generalized eigenvalue problem. Only a few methods in literature were developed to solve the problem of concentric ellipses. Here we study the statistical properties of existing methods by firstly establishing a general mathematical and statistical framework for this problem. Using rigorous perturbation analysis, we derive the variances and biasedness of each method under the small-sigma model. We also develop new estimators, which can be used as reliable initial guesses for other iterative methods. Then we compare the performance of each method according to their theoretical accuracy. Not only do our methods described here outperform other existing non-iterative methods, they are also quite robust against large noise. These methods and their practical performances are assessed by a series of numerical experiments on both synthetic and real data.