共查询到19条相似文献,搜索用时 62 毫秒
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为保证在去除点云数据噪声的同时不损失模型的细节特征,提出了一种基于特征信息的加权模糊C均值聚类去噪算法。首先,构建点云K-D树拓扑结构,根据点的r半径球邻域点统计特性去除大尺度离群噪声点。然后,利用主元分析法估算点云的曲率和法向量,根据曲率特征标识点云数据的特征区域,并采用特征加权模糊C均值聚类算法对特征区域去噪,采用加权模糊C均值聚类算法对非特征区域去噪。最后,使用双边滤波器对点云模型进行平滑。对提出的算法进行了验证实验,结果显示:去噪后点云模型的最大偏差保持在模型尺寸的0.15%以内;标准偏差保持在模型尺寸的0.03%以内。本文算法能够在有效去除不同尺度和强度的噪声的同时不损失点云模型的细节特征,去噪精度高,且对不同的噪声模型具有较强的鲁棒性。 相似文献
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三维激光扫描设备可以提供航空发动机外形实测点云,但其中包含的噪声会直接影响后期外形几何模型的重建精度。为保证在去除噪声的同时不模糊或破坏掉发动机复杂的外形几何特征,提出了一种基于深度学习的点云保特征去噪方法。将航空发动机外形噪声点云分割成特征数据和非特征数据之后,分别设计了特征去噪网络和非特征去噪网络,用于预测特征噪声点和非特征噪声点的位置修正向量,噪声点沿预测向量移动后被投影回模型真实的底层表面上,实现去噪。构建了用于特征去噪学习和非特征去噪学习的数据集。验证结果表明,在将该方法应用于各种噪声尺度的发动机外形点云时,相比现有的学习基方法,去噪效果得到提高,且有更好的几何特征保护能力,可以为后续重建提供高质量点云。 相似文献
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基于小波变换的信号去噪研究 总被引:2,自引:0,他引:2
介绍了小波变换理论,系统地研究了小波变换在信号处理尤其是信号滤波去噪方面的应用。根据不同类型的噪音.给出了基于不同小波变换的滤波算法并且对基于小波变换的滤波原理进行了分析。 相似文献
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基于信息向量机的机载激光雷达点云数据分类 总被引:1,自引:0,他引:1
针对支持向量机应用于机载激光雷达(LiDAR)点云数据分类时存在的模型稀疏性弱、预测结果缺乏概率意义、训练时间长等缺点,提出一种基于信息向量机的LiDAR点云数据分类算法。该算法采取假定密度滤波算法进行近似逼近,将分类问题转化为回归问题;以最大后验微分熵为依据,选择LiDAR点云数据活动子集信息向量实现模型稀疏化;最后,通过边缘似然最大化进行核函数自适应获取,选择一对余分类方法实现了点云数据多类分类。利用Niagara地区和非洲某地区点云数据进行了对比实验。结果表明:与支持向量机方法相比,基于信息向量机分类方法的分类精度分别提高到94.20%和90.78%,基向量数量分别减少到50个和90个,训练时间分别降低到5.86s和8.03s。实验结果验证了基于信息向量机的点云数据分类算法具有训练速度快、模型稀疏性强、分类精度高等优点。 相似文献
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因点云数据中存在噪声,通常对不同特征的点云数据采用相同的处理方法,虽然能删除噪声但也会因删除尖锐特征造成过光顺。提出了一种基于模糊C均值(FCM)聚类算法且均值滤波的点云去噪算法。该算法使用模糊C均值聚类算法删除大尺度噪声后,再将均值滤波应用到点云光顺中,对数据点中的小尺度噪声进行光顺。实验结果表明,该算法去噪效果明显,在去噪光顺过程中较好地保持了边界特征,也避免了过光顺问题的产生。 相似文献
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基于特征的零件信息模型 总被引:1,自引:1,他引:1
特征建模技术是CAD/CAPP/CAM集成的关键技术之一,特征模型使CAD、CAPP、CAM等系统共享统一的产品模型成为可能。文章介绍特征的概念和分类以及基于特征的零件信息模型的结构和构造方法。 相似文献
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Fault classification based upon vibration measurements is an essential building block of a conditional based health usage monitoring system. Multiple sensors are incorporated to assure the redundancy and to achieve the desired reliability and accuracy. The shortcoming of using multiple sensors is the need to deal with a high dimensional feature set, a computationally expensive task in classification. It is vital to reduce the feature dimension via an effective feature extraction and feature selection algorithm. A simple wavelet based feature selection scheme is proposed herein, uniquely built by local discriminant bases and genetic optimization. This scheme overcomes the disadvantages faced by the existing feature selection methods by producing a generic feature set, reducing the dimensionality of features, and requiring no prior information of the problem domain. The proposed feature selection scheme is based upon the strategy of "divide and conquer" that significantly reduce the computation time without compromising the classification performance. The simulation results show the proposed feature selection scheme provides at least 65% reduction of the total number of features at no cost of the classification accuracy. 相似文献
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基于几何与公差信息的加工特征识别方法 总被引:1,自引:0,他引:1
为实现计算机辅助设计与计算机辅助工艺规划系统的有效集成,提出了一种同时利用几何信息和公差信息的加工特征识别新方法。建立了加工资源、加工表面和加工方法三类信息模型。提出了切削模式的概念及以之为基础的表面加工方法生成原理和过程。建立了表面加工方法优化选择模型。采用多目标模糊优化结合蚁群算法求解该模型,为每个加工表面选择最优加工方法,并将在同次装夹中采用同一刀具类型和加工条件进行加工的表面聚为加工特征。最后,通过实例测试,验证了该方法的正确性和有效性。 相似文献
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Xue-Chang Zhang Jun-Tong Xi Jun-Qi Yan 《The International Journal of Advanced Manufacturing Technology》2006,30(1-2):70-75
It is necessary to smooth point cloud data in reverse engineering or the inspection of free-form surfaces because noisy points will have a negative influence on the post-processing of this data. The big problem in smoothing point cloud data is how to solve the dilemma between removing noisy points and keeping feature boundary information, whilst controlling the diffusiveness of noisy points. In this paper, the theory of anisotropic heat conduction is adopted to establish a mathematical model of point cloud data smoothing. The point cloud data can be considered as a temperature field with an adiabatic boundary. So the heat is only conducted inside the temperature filed and has no effect on the outer side. For point cloud data, it means that the smoothing is only on the local area, which makes a good balance between deleting noisy points and keeping feature boundary information. The method has been implemented by using two cases for practical application, and the result proves its efficiency. 相似文献
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《Measurement》2016
Point cloud data extraction is an important process in scan-tracking measurement. In this paper, a new method of on-line three-dimensional point cloud data extraction for scan-tracking measurement is proposed for reducing extremely dense sampled data while maintaining data accuracy during the real-time scan-tracking measuring process. It is inspired from sketch paintings: First outlining the broad contour of the curve and then revising local details till the interpolated curve satisfies the required accuracy. This method adopts bi-Akima spline interpolation for connecting acquired points in NC machining or for point data fitting in reverse engineering. It can reduce efficiently the amount of point data with a smaller data reduction ratio and a smoother machined/fitted surface than conventional three-dimensional chordal method. 相似文献