A Comparative Study of SIFT and SURF Algorithms under Different Object and Background Conditions

Feature detection and feature matching have been essential parts of Computer Vision algorithms. Feature detection algorithms like Scale Invariant Feature Transform (SIFT) form the basis of every feature extraction algorithm proposed till date. Since SIFT was proposed, researchers are continuously exploring the possibilities with it. It is one of the most prominently used algorithm or feature matching because of its invariance to scale. One of the other widely used algorithm in Computer Vision is Speeded up Robust features (SURF). In this paper, SIFT and SURF algorithms are compared and analysed under different object and background conditions. The SIFT algorithm performs better than SURF under blur and illumination changes. It also holds true for two different images where one image is being subjected to such property changes. The SURF will always perform faster than SIFT.