颅骨点云模型的优化配准

赵夫群; 周明全

doi:10.3788/OPE.20172507.1927

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颅骨点云模型的优化配准

信息科学 | 更新时间：2020-07-07

- 颅骨点云模型的优化配准
- Optimization registration of point cloud model of skull
- 光学精密工程 2017年25卷第7期页码：1927-1933
- 作者机构：
  
  1.咸阳师范学院教育科学学院, 陕西咸阳 712000
  2.西北大学信息科学与技术学院, 陕西西安 710127
  3.北京师范大学信息科学与技术学院, 北京 100875
- 作者简介：
  
  [ "赵夫群(1982-), 女, 山东临沂人, 博士研究生, 讲师, 2004年、2007年于西安石油大学分别获得学士学位、硕士学位, 主要从事图形图像处理和三维建模等方面的研究。E-mail:fuqunzhao@126.com" ]
  [ "周明全(1954-), 男, 陕西西安人, 教授, 博士生导师, 1976年、1988年于西北大学分别获得学士、硕士学位, 主要从事计算机可视化技术、软件工程、中文信息处理等领域的基础理论及其应用工程创新的研究。E-mail:mqzhou@nwu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(61373117);国家自然科学基金资助项目(61305032)
- DOI：10.3788/OPE.20172507.1927
  中图分类号： TP317.4
- 收稿日期：2016-12-30，
  
  录用日期：2017-2-17，
  
  纸质出版日期：2017-07-25
- 稿件说明：
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赵夫群, 周明全. 颅骨点云模型的优化配准[J]. 光学精密工程, 2017,25(7):1927-1933.

Fu-qun ZHAO, Ming-quan ZHOU. Optimization registration of point cloud model of skull[J]. Optics and precision engineering, 2017, 25(7): 1927-1933.
赵夫群, 周明全. 颅骨点云模型的优化配准[J]. 光学精密工程, 2017,25(7):1927-1933. DOI： 10.3788/OPE.20172507.1927.

Fu-qun ZHAO, Ming-quan ZHOU. Optimization registration of point cloud model of skull[J]. Optics and precision engineering, 2017, 25(7): 1927-1933. DOI： 10.3788/OPE.20172507.1927.

摘要

由于颅骨的三维点云数据模型复杂且不同人的颅骨差异较小，对其配准精度要求较高。为了提高颅骨点云模型的配准精度和收敛速度，提出了一种先粗配准再细配准的配准方法。首先，对颅骨点云数据模型进行去噪、简化和归一化等预处理；然后，通过区域划分、区域配准和求解组合系数以及求解刚体变换等步骤实现区域层次上的颅骨粗配准；最后，通过引入动态迭代系数来改进基于旋转角约束的迭代最近点算法，并采用该改进的ICP算法实现颅骨的细配准，从而达到精确配准的目的。实验结果表明：与ICP算法相比，改进的ICP算法的配准精度和收敛速度分别提高了约30%和50%。证明该种先粗配准再细配准的颅骨点云模型配准方法是一种精度高、速度快的有效颅骨配准算法。

Abstract

As the three dimensional point cloud model for skull is complex and there is little difference in skulls of different people

it has high requirement for the registration accuracy. In order to improve the registration accuracy and the convergence rate of point cloud model for skull

a kind of registration method of coarse regulation first and fine regulation second was proposed. Firstly

the point cloud model for skull should be subject to de-noising

simplification

normalization and other pretreatments; Then

based on regional partition

regional regulation

solving combination coefficient

solving rigid body transformation and other steps

coarse regulation for skull in regional level was realized; Finally

through introducing dynamic iteration coefficient algorithm

the iterative closest point based on the constraint of rotation angle was promoted; and improved ICP algorithm was used to realize the fine regulation for skull in order to achieve the purpose of accurate registration. The experimental result shows that comparing with ICP algorithm

the registration accuracy and the convergence rate of the improved ICP algorithm are separately improved about 30% and 50%. therefore

the kind of registration method of point cloud model for skull of coarse regulation first and fine regulation second is an effective skull regulation algorithm with high accuracy and fast speed.

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