语义知识约束的三维人体特征点检测和分割  

3D Human Landmark Detection and Segmentation with Semantic Knowledge

作　　者：陈启华[1] 潘翔[2] 张三元[3] 

机构地区：[1]浙江工业大学机械工程学院,杭州310014 [2]浙江工业大学计算机科学与技术学院,杭州310014 [3]浙江大学计算机科学与技术学院,杭州310027

出　　处：《计算机辅助设计与图形学学报》2011年第6期1061-1068,共8页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(60703001);浙江省自然科学基金(Y1110780)

摘　　要：针对三维人体模形内在知识理解问题,提出域知识约束下的三维人体特征点检测和层次分割算法.首先给出人体域知识的语义特征点定义及参数化描述;然后根据域知识和几何属性自动识别三维人体模型语义特征点,并根据语义特征点和测地距离对人体模型进行层次分割;最后通过图论对分割结果进行优化,并采用形状索引对人体特征点局部特性进行分析,提高分割和特征点的准确性.实验结果表明,文中算法不仅对三维人体模型姿态变化具有很好的稳定性,而且能有效地避免已有算法所出现的分割结果不一致问题.This paper addresses the problem about interior knowledge understanding of 3D human model.It proposes a new algorithm to detect 3D human landmarks and get hierarchical segmentation result by using semantic knowledge.Firstly,the algorithm formulates a domain knowledge representation by using 3D human landmarks and their parametric definition.Secondly,based on domain knowledge and geometry attribute,the algorithm automatically recognizes semantic landmarks of the input human model.In addition,the hierarchical segmentation result of the input model can be easily obtained by using detected semantic landmarks and geodesic distance.Finally,graph cut is used to refine coarse segmentation result and the shape index is used to improve the detection accuracy of landmarks.In experiment,the proposed algorithm shows very robust under human pose variation.Furthermore,it can avoid the inconsistent segmentation problem,which often occurs in the existing algorithms.

关 键 词：三维人体模型 域知识 人体特征点 层次分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]