Research On The Technique Of Curve Matching Based On Object Contour

The best matching of two curves is the central important problem in the fields of computer vision, image analysis and pattern recognition, which also can be applied in many other fields, such as character recognition, object recognition, image retrieval and diagnose. This paper aims at the research of curve matching approach and some corresponding techniques, and the application in reconstruction of archaeological fragments. The research work can be organized in the following aspects.1. An important application of shape matching technique is the reconstruction of archaeological fragments.Conceming this application, a series of processing algorithms is presented. An efficient algorithm of fragment digital, mesh simplification, extracting mesh outlines is presented, and the mesh simplification algorithm with features of 3D edge preservation by applying edge operations, In order to reduce the difference, smooth, filter and resample are applied on the original information.2. We introduced a new polygon periodic discrete curvature function based on curve continue curvature concept in 2D contour matching, the periodic discrete curvature has local, rotation and translation invariant. Applying efficient techniques to find the proper matching substrings, we also applied the hashing technique to find long matching substrings.3. According to the spline theory we presented a shape matching algorithm based on the similarity matrix of curvature and torsion values of 3D curve, we reduced the 3-D curve matching task into a 1-D string matching problem, which makes the matching more veracious and can be used on the 2D or 3D curve matching. In order to reduce the cost of matching, we used multiple scale technique.4. We presented a novel method of searching for similar fragments of 3D curves, with this method, a hash vector is associated with each fixed-length fragments of 3D sherd. Each vector consists of low frequence component of Fourier-Like spectrum for the distance between profile curve and the centroid. Then we can analyze the similarity of two fragments by evaluating the difference between hash vectors. The novel aspect of the method is that the following property is proved theoretically: if the distance between two fragments of curve is small, the distance between the hash vectors is small.5. In the process of reassembly of fragments, we provided virtual display and edit technique to afford convenience for user and ensure the result.In the end of the paper, we give a conclusion and tell the possible range of the curve matching methods.This research is supported by the National Natural Science Foundation of China and the National High Technology Development 863 program of China.