DARTs: Efficient scale-space extraction of DAISY keypoints

Winder et al. [15, 14] have recently shown the superiority of the DAISY descriptor [12] in comparison to other widely extended descriptors such as SIFT [8] and SURF [1]. Motivated by those results, we present a novel algorithm that extracts viewpoint and illumination invariant keypoints and describes them with a particular implementation of a DAISY-like layout. We demonstrate how to efficiently compute the scale-space and re-use this information for the descriptor. Comparison to similar approaches such as SIFT and SURF show higher precision vs recall performance of the proposed method. Moreover, we dramatically reduce the computational cost by a factor of 6x and 3x, respectively. We also prove the use of the proposed method for computer vision applications.