Variable precision camera calibration using neural network

LIU Hong-jian; LUO Yi; LIU Yun-cai

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Variable precision camera calibration using neural network

更新时间：2020-08-12

- Variable precision camera calibration using neural network
- Optics and Precision Engineering Vol. 12, Issue 4, Pages: 443-448(2004)
- 作者机构：
  
  上海交通大学, 图像处理与模式识别研究所上海,200030
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN948.41
- Received：16 February 2004，
  
  Revised：20 June 2004，
  
  Published Online：15 August 2004，
  
  Published：15 August 2004
- 稿件说明：
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刘宏建, 罗毅, 刘允才. 可变精度的神经网络摄像机标定法[J]. 光学精密工程, 2004,(4): 443-448

LIU Hong-jian, LUO Yi, LIU Yun-cai. Variable precision camera calibration using neural network[J]. Editorial Office of Optics and Precision Engineering, 2004,(4): 443-448
刘宏建, 罗毅, 刘允才. 可变精度的神经网络摄像机标定法[J]. 光学精密工程, 2004,(4): 443-448 DOI：

LIU Hong-jian, LUO Yi, LIU Yun-cai. Variable precision camera calibration using neural network[J]. Editorial Office of Optics and Precision Engineering, 2004,(4): 443-448 DOI：

摘要

提出了一种提高摄像机标定精度的方法.通过摄像机径向畸变模型

建立根据畸变严重程度自动改变区域划分数目的方法

对远离图像中心畸变程度严重的区域

划分细密;而靠近图像中心畸变轻微的区域

划分粗疏.通过对摄像机径向畸变区域进行划分

并且对每个畸变区域的像素进行单独的处理

构造相应的神经网络

得到整个畸变区域的处理结果

并对于不同的划分结果进行比较分析.分析比较得出:采用可变精度的神经网络摄像机标定法

可以大幅度提高标定的精度

划分数目越多

标定的精度越高

实验中识别率最高可达到99.45%.

Abstract

A method is proposed to improve the precision of camera calibration. Based on the radial distortion model of a camera

the approach is presented

in which the partition number of distortion region can be adjusted automatically. In a region far from the image center

where distortion is high

the number of partition is big. While in the region near the image center

where the distortion is low

the number is small. Through the partition of the camera distortion regions and processing pixels in the corresponding regions

the neural network can be built. Then

the calibration result can be obtained. The processed results of the novel method were compared with different partitions. The conclusion is that the number of partition is bigger and the calibration precision is higher. The precision can reach 99.45% at the maximal partitions.

关键词

Keywords

references

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