Abstract
Fast and robust image processing and matching is a very important task with various applications in computer vision and robotics. In this paper, we compare the performance of two different image matching techniques, i.e., by speed up robust features and by rotated robust independent elementary features, against different kinds of transformations and deformations such as scaling, rotation, noise, fisheye distortion, and cropping. 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.
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