Quadratic Matching Algorithm of Ancient Ceramic Fragments Based on Concavity-Convexity and Steering Angle
Fragment splicing is the key work in ancient ceramic repair.To address the problems of the random shapes,large number,weak surface textures,and local defects of ancient ceramic fragments(which result in a low accuracy and long matching time of the algorithm),this paper proposes a quadratic matching algorithm of ancient ceramic fragments based on concavity-convexity and steering angle.On the basis of extracting the contour curves of ancient ceramic fragments,the algorithm realizes the two-pair matching of fragments by combining coarse matching and fine matching.First coarse matching:The algorithm approximates the fragment profile curve through the polygon first to reduce the complexity of the profile.Then,it extracts the vertex concavity-convexity and vertex steering angle of the polygon to construct the first contour feature set.Finally,it uses the initial matching algorithm of the dual-modal features of concave-convex complementarity and traversal vertex alignment to determine the approximate matching segment,and the coarse matching point set is obtained.Quadratic fine matching:First,the algorithm selects any two adjacent points in the coarse matching points selected randomly to extract the fragment contour fragment.This reduces the number of contour points and improves the efficiency of the algorithm.Then,it calculates the contour steering angle of the contour fragment to extract the secondary profile feature set.Finally,it uses the quadratic matching based on Particle Swarm Optimization(PSO)to search for the exact matching segment and obtain the fine matching point set.The experimental results reveal that the algorithm displays a good stitching effect and strong robustness for the splicing of two-dimensional ancient ceramic fragments.The splicing error is less than 2％,and the running time efficiency is high(8％-20％higher than that of the existing algorithm).
fragment splicingquadratic matching algorithmcontour extractionconcavity-convexitysteering angleParticle Swarm Optimization(PSO)
万方数据
维普
