ISSN 1004-4140
CN 11-3017/P

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于CT数据的二次曲面拟合算法研究

樊知轩 阙介民 魏存峰 刘宝东 魏彪

樊知轩, 阙介民, 魏存峰, 等. 基于CT数据的二次曲面拟合算法研究[J]. CT理论与应用研究, 2023, 32(1): 35-42. DOI: 10.15953/j.ctta.2022.040
引用本文: 樊知轩, 阙介民, 魏存峰, 等. 基于CT数据的二次曲面拟合算法研究[J]. CT理论与应用研究, 2023, 32(1): 35-42. DOI: 10.15953/j.ctta.2022.040
FAN Z X, QUE J M, WEI C F, et al. Research on Quadric Surface Fitting Algorithm Based on CT Data[J]. CT Theory and Applications, 2023, 32(1): 35-42. DOI: 10.15953/j.ctta.2022.040. (in Chinese)
Citation: FAN Z X, QUE J M, WEI C F, et al. Research on Quadric Surface Fitting Algorithm Based on CT Data[J]. CT Theory and Applications, 2023, 32(1): 35-42. DOI: 10.15953/j.ctta.2022.040. (in Chinese)

基于CT数据的二次曲面拟合算法研究

doi: 10.15953/j.ctta.2022.040
基金项目: 中国科学院科研仪器设备研制项目(固液界面可视可控的光学晶体生长设备的研制(E12821V1))
详细信息
    作者简介:

    樊知轩:男,重庆大学控制工程专业在读硕士研究生,主要从事X射线成像物理、CT成像数学算法及工程应用等方面的研究,E-mail:124356321@qq.com

    刘宝东:男,博士,中国科学院高能物理研究所副研究员,主要从事X射线成像物理、CT成像数学算法及工程应用等方面的研究,E-mail:liubd@ihep.ac.cn

    魏彪:男,博士,重庆大学光电工程学院教授、博士生导师,重庆大学ICT无损检测教育部工程研究中心副主任,主要从事X射线成像物理、CT成像数学算法及工程应用等方面的研究,E-mail:weibiao@cqu.edu.cn

    通讯作者:

    男,博士,中国科学院高能物理研究所副研究员,主要从事X射线成像物理、CT成像数学算法及工程应用等方面的研究,E-mail:liubd@ihep.ac.cn

    男,博士,重庆大学光电工程学院教授、博士生导师,重庆大学ICT无损检测教育部工程研究中心副主任,主要从事X射线成像物理、CT成像数学算法及工程应用等方面的研究,E-mail:weibiao@cqu.edu.cn

  • 中图分类号: O  242; TP  391.41

Research on Quadric Surface Fitting Algorithm Based on CT Data

  • 摘要: 二次曲面工件在工业中比较常见,为测量物体内部的二次曲面,本文采用工业计算机断层成像(CT)技术获取物体的断层图像序列,利用U-net图像分割网络获得断层图像上的目标区域,对分割结果的边缘进行检测和曲线拟合并堆叠成三维点集,通过曲面拟合获取物体曲面的三维空间坐标信息。研究结果表明,本文的方法能够有效地实现边界提取和界面参数的拟合工作,拟合误差在1% 以内,相比传统方法有较大改进。

     

  • 图  1  模体示意图

    Figure  1.  Schematic of the model

    图  2  拟合流程图

    Figure  2.  Fitting process

    图  3  U-net网络结构图

    Figure  3.  U-net network structure diagram

    图  4  数据预处理流程图

    Figure  4.  Data preprocessing process

    图  5  U-net分割结果图

    Figure  5.  U-net segmentation result

    图  6  区域生长分割结果图

    Figure  6.  Region growing segmentation results

    图  7  边缘检测结果图

    Figure  7.  Edge detection result graphs

    图  8  椭圆拟合结果图

    Figure  8.  Ellipse fitting result diagrams

    图  9  三维点集图

    Figure  9.  3D point set diagram

    表  1  拟合结果

    Table  1.   Fitting results

    数据精简方式曲面高度Δh/mm 曲面曲率δ/%
    模体值拟合值相对误差/%模体值拟合值相对误差/%
    进行异常数据剔除14.614.670.40 0.09730.09780.40
    不进行不变层去除14.615.043.010.09730.10023.01
    不进行稳定层去除14.613.398.290.09730.08928.29
    下载: 导出CSV
  • [1] SENGUTTUVAN N, AOSHIMA M, SUMIYA K, et al. Oriented growth of large size calcium Fluoride single crystals for optical lithography[J]. Journal of Crystal Growth, 2005, 280(3/4): 462−466.
    [2] TANDJAOUI A, MANGELINCK-NOEL N, REINHART G, et al. Investigation of grain boundary grooves at the solid-liquid interface during directional solidification of multi-crystalline silicon: In situ characterization by X-ray imaging[J]. Journal of Crystal Growth, 2013, 377(15): 203−211.
    [3] DONG Y, SHUAI S, ZHENG T, et al. In-situ observation of solid-liquid interface transition during directional solidification of Al-Zn alloy via X-ray imaging[J]. Journal of Materials Science and Technology, 2020, 39: 113−123. doi: 10.1016/j.jmst.2019.06.026
    [4] 陈福泉, 林樽达, 余炳和. 盐封下降法生长KRS-5晶体[J]. 激光与红外, 1981,(9): 50−56.

    CHEN F Q, LIN Z D, YU B H. Growth of KRS-5 crystal by salt sealing down method[J]. Laser & Infrared, 1981, (9): 50−56. (in Chinese).
    [5] 李晓辉. 坩埚下降法CaF_2基晶体生长的数值模拟及缺陷研究[D]. 北京: 中国科学院大学, 2020.

    LI X H. Numerical simulation and defect analysis for the growth of alkaline-earth fluorides[D]. Beijing: University of Chinese Academy of Sciences, 2020. (in Chinese).
    [6] 曹冰, 何朝明, 李柏林. 工业CT图像轮廓数据提取研究[J]. 机电工程技术, 2009,(9): 3.

    CAO B, HE C M, LI B L. Extraction of contour data based on industrial CT image[J]. Mechanical & Electrical Engineering Technology, 2009, (9): 3. (in Chinese).
    [7] LUKÁCS G, MARTIN R R, MARSHALL A D. Faithful least-squares fitting of spheres, cylinders, cones and tori for reliable segmentation[C]//Computer Vision - ECCV'98, 5th European Conference on Computer Vision, Freiburg, Germany, June 2-6, 1998, Proceedings, Volume I. Springer Berlin Heidelberg, 1998.
    [8] 邹斌, 曾理, 马睿. 工业CT图像管道拟合[J]. 计算机工程与应用, 2010,46(11): 208−210. doi: 10.3778/j.issn.1002-8331.2010.11.063

    ZOU B, ZENG L, MA R. Fiting of industrial CT image pipes[J]. Computer Engineering and Applications, 2010, 46(11): 208−210. (in Chinese). doi: 10.3778/j.issn.1002-8331.2010.11.063
    [9] 卢艳平, 王珏, 覃仁超. 一种剥皮算法在工业CT图像分割中的应用[J]. 无损检测, 2005,27(5): 235−237. doi: 10.3969/j.issn.1000-6656.2005.05.004

    LU Y P, WANG J, QIN R C. Application of peeling method to segmentation of industrial computed tomographic image[J]. Nondestructive Testing, 2005, 27(5): 235−237. (in Chinese). doi: 10.3969/j.issn.1000-6656.2005.05.004
    [10] KOHLER R. A segmentation system based on thresholding[J]. Computer Graphics and Image Processing, 1981, 15(4): 319−338. doi: 10.1016/S0146-664X(81)80015-9
    [11] ADAMS R, BISHOF L. Seeded region growing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(6): 641-647.
    [12] SHEN X, SPANN M. Segmentation of 2D and 3D images through a hierarchical clusteringbased on region modelling[C]//Image Processing, 1997. Proceedings. International Conference on. IEEE, 1997.
    [13] SHELHAMER E, JONATHAN L, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640−651.
    [14] RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[J]. Springer International Publishing, 2015.
    [15] ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[J]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017, 6230-6239.
    [16] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[C]//Computer Science. 2014: 357-361.
    [17] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834−848. doi: 10.1109/TPAMI.2017.2699184
    [18] CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation[J]. arXiv preprint arXiv: 1706.05587, 2017.
    [19] FITZGIBBON A, PILU M, FISHER R B. Direct least square fitting of ellipses[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1999.
    [20] MICELI A, THIERRY R, BETTUZZI M, et al. Comparison of simulated and measured spectra of an industrial 450 kV X-ray tube[J]. Nuclear Instruments & Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, 2007, 580(1): 123-126.
  • 加载中
图(9) / 表(1)
计量
  • 文章访问数:  136
  • HTML全文浏览量:  76
  • PDF下载量:  17
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-03-09
  • 修回日期:  2022-04-03
  • 录用日期:  2022-04-12
  • 网络出版日期:  2022-05-31
  • 刊出日期:  2023-01-31

目录

    /

    返回文章
    返回