M. P. Mukhina, T. A. Yeremeieva


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.


Image matching; image feature; robust matching; image distortion


B. Moghaddam, C. Nastar and A. Pentland, “A Bayesian similarity measure for deformable image matching,” Image and Vision Computing, vol. 19, no. 5, pp. 235–244, 2001.

B. Shan, “A Novel Image Correlation Matching Approach,” JMM, vol. 5, no. 3, 2010.

K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, A comparison of affine region detectors. International Journal of Computer Vision, 65(1/2):43–72, 2005.

David G Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol.50, no. 2, 2004, pp. 91–110.

Herbert Bay, Tinne Tuytelaars and Luc Van Gool, "Speeded-up robust features (SURF)," Computer vision and image understanding, vol.110, no.3, 2008, pp. 346–359.

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.