Statistical Optimization for Geometric Fitting: Theoretical Accuracy Bound and High Order Error Analysis

Statistical Optimization for Geometric Fitting: Theoretical Accuracy Bound and High Order Error Analysis

Abstract

A rigorous accuracy analysis is given to various techniques for estimating parameters of geometric models from noisy data for computer vision applications. First, it is pointed out that parameter estimation for vision applications is very different in nature from traditional statistical analysis and hence a different mathematical framework is necessary in such a domain. After general theories on estimation and accuracy are given, typical existing techniques are selected, and their accuracy is evaluated up to higher order terms. This leads to a “hyperaccurate” method that outperforms existing methods.