  Well-known corner or local extrema feature based detectors such as FAST and
DoG have achieved noticeable successes. However, detecting keypoints in the
presence of blur has remained to be an unresolved issue. As a matter of fact,
various kinds of blur (e.g., motion blur, out-of-focus, and space-variant)
remarkably increase challenges for keypoint detection. As a result, those
methods have limited performance. To settle this issue, we propose a
blur-countering method for detecting valid keypoints for various types and
degrees of blurred images. Specifically, we first present a distance metric for
derivative distributions, which preserves the distinctiveness of patch pairs
well under blur. We then model the asymmetry by utilizing the difference of
squared eigenvalues based on the distance metric. To make it scale-robust, we
also extend it to scale space. The proposed detector is efficient as the main
computational cost is the square of derivatives at each pixel. Extensive visual
and quantitative results show that our method outperforms current approaches
under different types and degrees of blur. Without any parallelization, our
implementation\footnote{We will make our code publicly available upon the
acceptance.} achieves real-time performance for low-resolution images (e.g.,
$320\times240$ pixel).
