  Fast binary descriptors build the core for many vision based applications
with real-time demands like object detection, Visual Odometry or SLAM. Commonly
it is assumed, that the acquired images and thus the patches extracted around
keypoints originate from a perspective projection ignoring image distortion or
completely different types of projections such as omnidirectional or fisheye.
Usually the deviations from a perfect perspective projection are corrected by
undistortion. Latter, however, introduces severe artifacts if the cameras
field-of-view gets larger. In this paper, we propose a distorted and masked
version of the BRIEF descriptor for calibrated cameras. Instead of correcting
the distortion holistically, we distort the binary tests and thus adapt the
descriptor to different image regions.
