Better Global Polynomial Approximation for Image Rectification

TL;DR

This paper introduces a new global polynomial approximation algorithm to improve image rectification by accurately correcting distortions and misalignments in images using polynomial fitting.

Contribution

The paper presents a novel algorithm for global polynomial approximation that enhances image rectification accuracy over previous methods.

Findings

Improved polynomial fitting accuracy for image correction

Effective correction of distortions and misalignments

Enhanced image rectification performance

Abstract

When using images to locate objects, there is the problem of correcting for distortion and misalignment in the images. An elegant way of solving this problem is to generate an error correcting function that maps points in an image to their corrected locations. We generate such a function by fitting a polynomial to a set of sample points. The objective is to identify a polynomial that passes "sufficiently close" to these points with "good" approximation of intermediate points. In the past, it has been difficult to achieve good global polynomial approximation using only sample points. We report on the development of a global polynomial approximation algorithm for solving this problem. Key Words: Polynomial approximation, interpolation, image rectification.