  In this paper, we focus on removing interference of motion blur by the
derivation of motion blur invariants.Unlike earlier work, we don't restore any
blurred image. Based on geometric moment and mathematical model of motion blur,
we prove that geometric moments of blurred image and original image are
linearly related. Depending on this property, we can analyse whether an
existing moment-based feature is invariant to motion blur. Surprisingly, we
find some geometric moment invariants are invariants to not only spatial
transform but also motion blur. Meanwhile, we test invariance and robustness of
these invariants using synthetic and real blur image datasets. And the results
show these invariants outperform some widely used blur moment invariants and
non-moment image features in image retrieval, classification and template
matching.

Computational Geometry;Computer Vision and Pattern Recognition