.png){kind=icon}
返回
Camera calibration optimization algorithm that uses a step function
.png){kind=icon}
OA
AI
DOI：10.1364/OE.516126.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Camera calibration is very important when planning machine vision tasks. Calibration may involve 3D reconstruction, size measurement, or careful target positioning. Calibration accuracy directly affects the accuracy of machine vision. The parameters in many image distortion models are usually applied to all image pixels. However, this may be associated with rather high pixel reprojection errors at image edges, compromising camera calibration accuracy. In this paper, we present a new camera calibration optimization algorithm that features a step function that splits images into center and edge regions. First, based on the increasing pixel reprojection errors according to the pixel distance away from the image center, we gave a flexible method to divide an image into two regions, center and boundary. Then, the algorithm automatically determines the step position, and the calibration model is rebuilt. The new model can calibrate the distortions at the center and boundary regions separately. Optimized by the method, the number of distortion parameters in the old model is doubled, and different parameters represent different distortions within two regions. In this way, our method can optimize traditional calibration models, which define a global model to describe the distortion of the whole image and get a higher calibration accuracy. Experimentally, the method significantly improved pixel reprojection accuracy, particularly at image edges. Simulations revealed that our method was more flexible than traditional methods.
Keyword:
MODEL
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
机构
引用论文
Comprehensive improvement of camera calibration based on mutation particle swarm optimization.png)
MEASUREMENT
MEASUREMENTIF5.6
PREAI
分享收藏Analysis of different parameters of influence in industrial cameras calibration processesMEASUREMENT
MEASUREMENTIF5.6
PREAI
分享收藏Constraints on general motions for camera calibration with one-dimensional objectsPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY随机样本共识-模型拟合的范例，可应用于图像分析和自动制图OAAI
OAAI分享收藏
查看更多内容