Recognizing Planar Curve Based on NRLCTI and Match Sub-curve

A new method is proposed to match planar curves under affine transformation in this paper. First, the definition of NRLCTI (normalized run length code of conner and tangent and inflexion points) of a two-dimensional curve is given. In terms of NRLCTI, we can match feature points both on object and models preliminarily. Then a method for estimating optimal affine transformation is given based on Frobenius norm. And then a new algorithm is designed to match sub-curves, which can cope with the problem that the curve represented by the feature points is not always unique. Last a novel approach is set up to recognize curves from a line drawing or image. By partitioning the curve into many sub-curve based on landmarks, then matching and recognizing them, the low accuracy for curve approximated by polygon or conies curve can be overcome. Computer simulations demonstrate the effectiveness of the algorithm preliminarily.