Yang Zeyin
Hefei Normal University, Hefei 230061, Anhui, China.
Int J Anal Chem. 2022 Jul 7;2022:5710535. doi: 10.1155/2022/5710535. eCollection 2022.
To improve the analysis ability of point cloud 3D reconstruction of sparse images of nano-ceramic sculpture points, an automatic cloud 3D reconstruction method of nano-ceramic sculpture points based on sparse image sequence is proposed. Firstly, 3D angle detection and edge contour feature extraction methods are used to analyze 3D point cloud features of nano-ceramic sculpture point save image; secondly, the point cloud of the fuel economy image of nano-ceramic sculpture points is merged and the sloping action method is used to shape degradation to realize the information increase and fusion filtering of the fuel economy image of nano-ceramic sculpture points; finally, combined with the local mean denoising method, image is refined to improve the ability of sparse image outline structure of nano-ceramic sculpture points. The simulation results show that this method has high accuracy, good image matching ability, and high signal-to-noise ratio.
为提高纳米陶瓷雕塑点稀疏图像的点云三维重建分析能力,提出了一种基于稀疏图像序列的纳米陶瓷雕塑点自动云三维重建方法。首先,采用三维角度检测和边缘轮廓特征提取方法分析纳米陶瓷雕塑点保存图像的三维点云特征;其次,对纳米陶瓷雕塑点的燃油经济性图像点云进行合并,并采用倾斜动作法进行形状退化,以实现纳米陶瓷雕塑点燃油经济性图像的信息增加和融合滤波;最后,结合局部均值去噪方法对图像进行细化,以提高纳米陶瓷雕塑点稀疏图像轮廓结构的能力。仿真结果表明,该方法具有较高的精度、良好的图像匹配能力和较高的信噪比。