激光与光电子学进展, 2020, 57 (4): 041511, 网络出版: 2020-02-20
基于多特征描述子自适应权重的文物碎片分类 下载: 737次
Classification of Cultural Fragments Based on Adaptive Weights of Multi-Feature Descriptions
碎片点云分类 多特征描述子 旋转投影 体积积分不变量 fragment point cloud classification multi-feature description rotating projection volume integral invariant
摘要
传统三维模型分类方法因依赖文物碎片的整体形状特征,用于对破损严重、细节缺失、形状不规则的文物碎片分类时,时效差、成本高、准确率低。而文物碎片特征点周围的局部曲面深度信息与表面的规律性几何纹理可作为分类的判别特征。据此,提出了一种局部点云信息与显著性多特征描述子,通过提取文物碎片表面的规律性几何特征,并结合旋转投影特征,作为文物碎片的分类判别特征;然后提出相似度度量准则,根据每类特征的度量结果,自适应地计算两种特征的权重,实现分类。使用兵马俑碎片数据集进行实验,结果表明,该方法占用内存小、计算速度快。利用多倍交叉法对结果进行校验,准确率达到74.78%,相较于传统三维模型匹配方法提高了15.64%。
Abstract
The traditional three-dimensional (3D) model classification method relies on the overall shape characteristics of cultural relics, which causes the problem of low efficiency, high cost and low accuracy for the classification of cultural debris with serious damage, missing details and irregular shapes. The depth information of local surface around the feature points of cultural relics and the regular geometric texture of the surface can be used as the discriminative features of classification. Therefore, a local point cloud information and significant multi-feature descriptor are proposed. The surface regularity geometric feature, combined with the rotation projection feature, are used as the discriminant features of the classification of cultural relics; then the similarity metric rule is proposed and the weight of two characteristics are adaptively calculated according to the measurement results of each type of feature, to achieve the classification of cultural debris. The debris data set of terracotta warriors is used as experimental data for the classification, the results show that the proposed method occupies small memory and calculates fast. Multiple cross-validation methods are used to verify the results, the accuracy rate is 74.78%, which is 15.64% higher than that of the traditional 3D model matching method.
陆正杰, 李纯辉, 耿国华, 周蓬勃, 李岩, 刘洋. 基于多特征描述子自适应权重的文物碎片分类[J]. 激光与光电子学进展, 2020, 57(4): 041511. Zhengjie Lu, Chunhui Li, Guohua Geng, PengBo Zhou, Yan Li, Yang Liu. Classification of Cultural Fragments Based on Adaptive Weights of Multi-Feature Descriptions[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041511.