Matching method of cultural relics fragments based on multi-feature parameters fusion

ZHAO Fuqun; ZHOU Mingquan

doi:10.37188/OPE.20233110.1522

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Matching method of cultural relics fragments based on multi-feature parameters fusion

Information Sciences | 更新时间：2023-05-25

- Matching method of cultural relics fragments based on multi-feature parameters fusion
- Optics and Precision Engineering Vol. 31, Issue 10, Pages: 1522-1531(2023)
- 作者机构：
  
  1.西安财经大学信息学院，陕西西安 710100
  2.西北大学信息科学与技术学院，陕西西安 710127
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233110.1522
  CLC： TP391
- Received：08 July 2022，
  
  Revised：30 September 2022，
  
  Published：25 May 2023
- 稿件说明：
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赵夫群,周明全.基于多特征参数融合的文物碎片自动匹配方法[J].光学精密工程,2023,31(10):1522-1531.

ZHAO Fuqun,ZHOU Mingquan.Matching method of cultural relics fragments based on multi-feature parameters fusion[J].Optics and Precision Engineering,2023,31(10):1522-1531.
赵夫群,周明全.基于多特征参数融合的文物碎片自动匹配方法[J].光学精密工程,2023,31(10):1522-1531. DOI： 10.37188/OPE.20233110.1522.

ZHAO Fuqun,ZHOU Mingquan.Matching method of cultural relics fragments based on multi-feature parameters fusion[J].Optics and Precision Engineering,2023,31(10):1522-1531. DOI： 10.37188/OPE.20233110.1522.

摘要

针对基于单一几何特征的文物碎片匹配方法的精度不高的问题，提出一种基于多特征参数融合的文物碎片自动匹配方法。首先，采用分割算法提取文物碎片的断裂面，并计算断裂面上点的四个特征参数：点到邻域点的平均距离、点到邻域重心的距离、曲率以及邻域法向夹角平均值；然后，融合四个特征参数得到特征判别参数，并通过判断特征判别参数值提取出特征点集；最后，采用基于尺度因子的迭代最近点（Iterative Closest Point， ICP）算法对特征点集进行匹配，从而实现文物碎片的断裂面匹配。实验采用兵马俑碎片的点云数据模型来验证该基于多特征参数融合的文物碎片匹配方法，结果表明该匹配方法可以克服基于单一几何特征匹配方法的精度不够高的问题，比已有算法的匹配精度提高15%以上，时间效率提高20%以上。因此说，该基于多特征参数融合的匹配方法是一种有效的文物碎片匹配方法。

Abstract

To address the low accuracy of the single-geometric-feature-based matching method for cultural relic fragments， an alternative multi-feature-parameter-fusion-based automatic matching method is proposed herein. For this， first， a segmentation algorithm is used to extract the fracture surfaces of cultural relic fragments， and the following four characteristic parameters of points on these fracture surfaces are computed： the average distance from a point to its neighborhood points， distance from a point to the gravity center of its neighborhood， curvature， and average value of the normal included angle of the neighborhood. Following this， the four feature parameters are fused to obtain feature discrimination parameters， and a feature point set is extracted by judging the value of these feature discrimination parameters. Finally， the iterative closest point algorithm based on the scale factor is used to match the feature point set， and consequently， accurate fracture surface matching of cultural relic fragments is achieved. In the experiment， a point cloud data model of Terracotta Warriors fragments is used to verify the performance of the multi-feature-parameter-fusion-based matching method for cultural relic fragments. The results reveal that the proposed matching method can overcome the low accuracy of the single-geometric-feature-based matching method. Compared with the matching accuracy of the existing algorithm， that of the proposed algorithm is improved by more than 15%， while its time efficiency is improved by more than 20%. Therefore， the multi-feature-parameter-fusion-based matching method is effective for cultural relic fragment matching.

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