  Rotational motion blur caused by the circular motion of the camera or/and
object is common in life. Identifying objects from images affected by
rotational motion blur is challenging because this image degradation severely
impacts image quality. Therefore, it is meaningful to develop image invariant
features under rotational motion blur and then use them in practical tasks,
such as object classification and template matching. This paper proposes a
novel method to generate image moment invariants under general rotational
motion blur and provides some instances. Further, we achieve their invariance
to similarity transform. To the best of our knowledge, this is the first time
that moment invariants for rotational motion blur have been proposed in the
literature. We conduct extensive experiments on various image datasets
disturbed by similarity transform and rotational motion blur to test these
invariants' numerical stability and robustness to image noise. We also
demonstrate their performance in image classification and handwritten digit
recognition. Current state-of-the-art blur moment invariants and deep neural
networks are chosen for comparison. Our results show that the moment invariants
proposed in this paper significantly outperform other features in various
tasks.
