Image feature points are the basis for numerous computer vision tasks, such as pose estimation or object detection. State of the art algorithms detect features that are invariant to scale and orientation changes. While feature detectors and descriptors have been widely studied in terms of stability and repeatability, their localisation error has often been assumed to be uniform and insignificant. We argue that this assumption does not hold for scale-invariant feature detectors and demonstrate that the detection of features at different image scales actually has an influence on the localisation accuracy. A general framework to determine the uncertainty of multi-scale image features is introduced. This uncertainty is represented via anisotropic covariances with varying orientation and magnitude. We apply our framework to the well-known SIFT and SURF algorithms, detail its implementation and make it available. Finally the usefulness of such covariance estimates for bundle adjustment and homography computation is illustrated.     «

Image feature points are the basis for numerous computer vision tasks, such as pose estimation or object detection. State of the art algorithms detect features that are invariant to scale and orientation changes. While feature detectors and descriptors have been widely studied in terms of stability and repeatability, their localisation error has often been assumed to be uniform and insignificant. We argue that this assumption does not hold for scale-invariant feature detectors and demonstrate th...     »