SIFT Vs SURF: Quantifying the Variation in Transformations
This work addresses the need for more precise evaluation of feature detection methods in computer vision, though it is incremental as it builds on existing comparisons.
The paper quantitatively analyzes the robustness of SIFT and SURF against various image transformations, such as rigid body and projective, by examining deformation effects rather than absolute transformations, providing empirical insights for selecting appropriate techniques in specific use cases.
This paper studies the robustness of SIFT and SURF against different image transforms (rigid body, similarity, affine and projective) by quantitatively analyzing the variations in the extent of transformations. Previous studies have been comparing the two techniques on absolute transformations rather than the specific amount of deformation caused by the transformation. The paper establishes an exhaustive empirical analysis of such deformations and matching capability of SIFT and SURF with variations in matching parameters and the amount of tolerance. This is helpful in choosing the specific use case for applying these techniques.