基于局部特征概率密度估计的三维模型特征提取方法  

New Architecture for Extraction of 3D Model Features Based on Probabilistic Density Estimation of Local Surface Features

作　　者：孙挺[1,2] 张锦华[2] 耿国华[1] 

机构地区：[1]西北大学可视化研究所,西安710069 [2]周口师范学院计算机科学与技术学院,周口466000

出　　处：《计算机科学》2015年第6期293-295,312,共4页Computer Science

基　　金：河南省科技发展计划科技攻关项目(122400450356);河南省科技发展计划软科学项目(132400410927;132400410934)资助

摘　　要：特征提取是三维模型检索中的关键。给出了基于局部特征概率密度估计的三维模型特征提取体系框架。针对三维表面局部几何特征集,利用核密度估计方法估计选定目标点的特定局部特征密度构成特征向量,用以描述三维模型。抽取三维网格模型的单元特征及多个单元特征组合而成的多元特征支持实现三维模型检索。实验验证了其检索性能优于基于统计的直方图特征提取方法。Feature extraction is a key issue for 3D model retrieval. A new architecture for extraction of 3D model fea- tures using probabilistic density estimation of local surface features was proposed. With the set of 3D local geometrical features, the local feature density of a chosen target point was evaluated using probabilistic density estimation methods. The 3D model can be described using the feature vector comprised of all local feature density values. The single-variate and multi-variate descriptors of 3D mesh model support the implementation of 3D model retrieval. The results show that the retrieval performance of the method is better than that of the statistical feature extraction methods.

关 键 词：概率密度估计 特征融合 特征提取 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]