Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images

TL;DR

This study compares the robustness and performance of SIFT, SURF, and ORB image matching techniques under various distortions to determine which method is most effective for different transformations.

Contribution

It provides a comprehensive performance comparison of SIFT, SURF, and ORB under multiple distortions, highlighting their robustness and efficiency in image matching tasks.

Findings

SIFT performs best under scale and rotation distortions.

SURF shows high robustness against noise and shearing.

ORB offers faster matching but less robustness compared to SIFT and SURF.

Abstract

Fast and robust image matching is a very important task with various applications in computer vision and robotics. In this paper, we compare the performance of three different image matching techniques, i.e., SIFT, SURF, and ORB, against different kinds of transformations and deformations such as scaling, rotation, noise, fish eye distortion, and shearing. For this purpose, we manually apply different types of transformations on original images and compute the matching evaluation parameters such as the number of key points in images, the matching rate, and the execution time required for each algorithm and we will show that which algorithm is the best more robust against each kind of distortion. Index Terms-Image matching, scale invariant feature transform (SIFT), speed up robust feature (SURF), robust independent elementary features (BRIEF), oriented FAST, rotated BRIEF (ORB).