Recognizing a Planar Curve Based on NRLCTI and Area Threshold

Abstract

In order to recognize that the curve has the same feature points but the curvature between the two adjacent feature points is different, a new method for matching the contour curve is presented in this chapter. First, the definition of NRLCTI (Normalized Run Length Code of Conner and Tangent and Inflexion Points) of a planar curve is given. In terms of NRLCTI, feature points both on object and models can be matched preliminarily. Subsequently, a new method is designed to match subcurves between the two adjacent feature points. We sample points on the subcurve based more on the precision requirement using the given minimal area threshold. A new recognition vector of sample points is defined, and a novel recognition vector matrix is constructed based on the recognition vector of sample points. Last, the dissimilarity measure of the corresponding subcurves is calculated by comparing the recognition vector matrix. The curve is recognized by matching all its subcurves. The method matches the object and model from simple to complex, so that many redundancies in calculations can be avoided. The experiment results show that the method is efficient and feasible.