Expectations of Random Sets and Their Boundaries Using Oriented Distance Functions

TL;DR

This paper introduces new definitions for the expected set and boundary in shape estimation using oriented distance functions, offering properties like convexity preservation and consistency in empirical estimation.

Contribution

It proposes novel expectation definitions based on oriented distance functions, including properties, empirical estimators, and loss functions for set inference.

Findings

Expected boundary estimator shown to be consistent

New expectation definitions preserve convexity and are equivariant

Empirical and theoretical examples illustrate the approach

Abstract

Shape estimation and object reconstruction are common problems in image analysis. Mathematically, viewing objects in the image plane as random sets reduces the problem of shape estimation to inference about sets. Currently existing definitions of the expected set rely on different criteria to construct the expectation. This paper introduces new definitions of the expected set and the expected boundary, based on oriented distance functions. The proposed expectations have a number of attractive properties, including inclusion relations, convexity preservation and equivariance with respect to rigid motions. The paper introduces a special class of separable oriented distance functions for parametric sets and gives the definition and properties of separable random closed sets. Further, the definitions of the empirical mean set and the empirical mean boundary are proposed and empirical evidence of the consistency of the boundary estimator is presented. In addition, the paper gives loss functions for set inference in frequentist framework and shows how some of the existing expectations arise naturally as optimal estimators. The proposed definitions of the set and boundary expectations are illustrated on theoretical examples and real data.