Determining ellipses from low-resolution images with a comprehensive image formation model

TL;DR

This paper introduces a detailed statistical model for accurately estimating the parameters of elliptic shapes from low-resolution, photon-limited images, outperforming standard methods and providing uncertainty measures.

Contribution

A novel comprehensive image formation model that improves parameter estimation accuracy for elliptic shapes in low-resolution images, applicable to various parametric shapes.

Findings

Achieves unprecedented accuracy in parameter estimation.

Provides a covariance matrix and confidence region for uncertainty quantification.

Demonstrates effectiveness on both simulated and real images.

Abstract

When determining the parameters of a parametric planar shape based on a single low-resolution image, common estimation paradigms lead to inaccurate parameter estimates. The reason behind poor estimation results is that standard estimation frameworks fail to model the image formation process at a sufficiently detailed level of analysis. We propose a new method for estimating the parameters of a planar elliptic shape based on a single photon-limited, low-resolution image. Our technique incorporates the effects of several elements - point-spread function, discretisation step, quantisation step, and photon noise - into a single cohesive and manageable statistical model. While we concentrate on the particular task of estimating the parameters of elliptic shapes, our ideas and methods have a much broader scope and can be used to address the problem of estimating the parameters of an arbitrary parametrically representable planar shape. Comprehensive experimental results on simulated and real imagery demonstrate that our approach yields parameter estimates with unprecedented accuracy. Furthermore, our method supplies a parameter covariance matrix as a measure of uncertainty for the estimated parameters, as well as a planar confidence region as a means for visualising the parameter uncertainty. The mathematical model developed in this paper may prove useful in a variety of disciplines which operate with imagery at the limits of resolution.