Image registration comparative analysis: normalized correlation versus SIFT-based registration

V.A Dushepa,I.V Baryshev,Y.A Tiahnyriadno

doi:10.30837/rt.2020.4.203.21

Abstract

The paper compares the image registration algorithms: the classical normalized correlation (as a representative of intensity-based algorithms) and the SIFT-based algorithm (feature-based registration). A gradient subpixel correction algorithm was also used for normalized correlation. We compared the effectiveness of their work on real images (including a terrain map) when modeling artificial distortions. The accuracy of determining the position (shift) of one image relative to another in the presence of rotation and scale changes was studied. The experiment was carried out using a simulation model created in the Python programming language using the OpenCV computer vision library. The results of the experiments show that in the absence of rotation and scale changes between the registered images the normalized correlation provides a slightly smaller root-mean-square error. At the same time, if there are even small such distortions, for example, a rotation of more than 2 degrees and a scale change of more than 2 percent, the probability of correct registration for the normalized correlation drops sharply. It was also noted that the advantages of normalized correlation are almost 5 times higher speed and the possibility of using it for small fragments (50x50 or less), where it is problematic for the SIFT algorithm to allocate a sufficient number of keypoints. It was also shown that the use of a two-stage algorithm (SIFT-based registration at the first stage, and optimization with normalized correlation as a criterion at the second) allows you to get both high accuracy and stability to rotation and scale change, but this will be accompanied by high computational costs.

Highlights

Until now, a large variety of different image registration algorithms have been developed
If we find correspondences between them, we can estimate the geometric transformation between the images
We considered the position of the current image (CI) center to be the coordinates of the CI

Summary

Introduction

A large variety of different image registration algorithms have been developed. Feature-based methods work quite stably with a variety of models of geometric transformations. Their accuracy is inferior to correlation methods. As a representative of the second group of methods, an algorithm that uses a SIFT detector (and a keypoint descriptor) was chosen [7]. In the second part of the work, it is shown (and quantified) that the use of a combined algorithm (using the SIFT at the first stage, and the NCC at the second stage) allows to join the advantages of both approaches and achieve high estimation accuracy for complex geometric transformation models. The novelty of this work is not the ideas of the approaches themselves, which are generally known, but specific numerical performance indicators calculated by a simulation experiment (including real terrain maps as images that are registered)

Simulation setup

NCC and SIFT for real images registration

Combining SIFT-based registration and NCC

Conclusions