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

By Ebrahim Karami, Siva Prasad, Mohamed S. Shehata. arXiv.org, 2017

398 citations recorded by the source corpus.

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).

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c4927b6bfb0013bdccc1f085c49a92bc26341c88

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Deep Learning vs. Traditional Computer Vision (2019, methodology): “There are many useful tools available to semi-automate the process as reviewed by [20], many of which take advantage of algorithmic approaches such as ORB features [55], polygon morphing [63], semi-automatic Area of Interest (AOI) fitting [55] and all of the above [63].”