| 基于广义变换矩阵的机械零件三维模型骨架匹配 |
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| 引用本文： | 朱文博,杨超,甘屹,陈龙.基于广义变换矩阵的机械零件三维模型骨架匹配[J].上海理工大学学报,2019,41(4):381-387. |
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| 作者姓名： | 朱文博 杨超 甘屹 陈龙 |
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| 作者单位： | 上海理工大学 机械工程学院, 上海 200093,上海理工大学 机械工程学院, 上海 200093,上海理工大学 机械工程学院, 上海 200093,上海理工大学 机械工程学院, 上海 200093 |
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| 摘 要： | 提出了一种基于广义变换矩阵的机械零件三维模型骨架匹配方法。骨架中两骨架点间的连线称为骨架枝,借鉴机器人学中广义连杆之间关系的表示方法,将骨架枝看成若干个连杆,在骨架点处建立固定坐标系及骨架枝坐标系,采用广义变换矩阵表示骨架枝。将广义变换矩阵转化成向量,引用统计学中的相关性度量方法,通过计算2个向量的皮尔逊相关系数得到2个广义变换矩阵的相似度,即得到2个骨架枝的相似度。搜索相匹配的骨架枝并计算整个骨架的相似度。通过实例验证和实验分析,表明该算法具有较快的检索速度和较高的准确度。
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| 关 键 词： | 机械零件 三维模型 骨架 广义变换矩阵 皮尔逊相关系数 相似度 |
| 收稿时间： | 2018/6/25 0:00:00 |
3D Model Skeleton Matching of Mechanical Parts Based on Generalized Transform Matrix |
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| ZHU Wenbo,YANG Chao,GAN Yi and CHEN Long.3D Model Skeleton Matching of Mechanical Parts Based on Generalized Transform Matrix[J].Journal of University of Shanghai For Science and Technology,2019,41(4):381-387. |
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| Authors: | ZHU Wenbo YANG Chao GAN Yi and CHEN Long |
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| Institution: | School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China,School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China,School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China and School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China |
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| Abstract: | A skeleton matching method for 3D models of mechanical parts based on generalized transformation matrix was proposed. The connection line between the two skeleton points is called the skeleton branch. Referring to the representation method of the relationship between the generalized connecting rods in the robotics, the skeleton branches are regarded as a number of connecting rods. A fixed coordinate system and a skeleton coordinate system are established at the skeleton point. The skeleton branch is represented by the generalized transformation matrix. The generalized transformation matrix is transformed into a vector. Referring to the method of correlation measurement in statistics, the similarity degree of the two generalized transformation matrices was obtained by calculating the Pearson correlation coefficients of two vectors, that is, the similarity degree of the two skeleton branches. Search the matching skeleton branches and calculate the similarity of the whole skeleton. Through the example verification and experimental analysis, the algorithm has a fast retrieval speed and good accuracy. |
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| Keywords: | mechanical parts 3D model skeleton generalized transformation matrix Pearson correlation coefficient similarity degree |
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