The relative motion between the camera and the scene leads to blurring of images. By appealing to traveling wave equations, the restoration of the motion-blurred images can be achieved and a criterion of objective quality assessment can be obtained for these restored versions even without any a priori knowledge of the original image. Most significantly, this criterion actually applies to any categories of image restoration methods as long as the image blur is caused by a linear uniform motion with good sensitivity and consistency.

In this paper a planar curve segmentation method based on the analysis of curvature and mean polygonal area is proposed. A rough segmentation of the curve is first produced by a high/low curvature classifier and then refined by a high/low/zero classifier and a knot/straight/convex/concave shape identifier so as to obtain a set of critical knots and shape descriptors for all segments. A straight line or conic approximation is then applied to each segment to generate a symbolic representation of the curve.