通过分析数字图像几何畸变产生的机制，提出一种数字图像几何畸变自动校正的方法。
According to the reasons for the geometrical distortion of digital images, an auto-correction method is proposed.
提出了虹膜自动身份识别的算法及虹膜图像几何畸变的校正方法，最后用相关系数来测度图像的匹配结果。
This paper proposes an algorithm of iris automated identification and a method for rectification of geometric distortion of iris image. The matching result is measured by the relative coefficient.
随后又介绍了导致运动图像模糊及几何畸变的原因及对应消除模糊的方法。
Introduced the causes of motion image blurring and geometric distortion and the methods to deblur.
配准问题的目的就是将同一场景的不同图像对齐或匹配，消除存在的几何畸变。
Basically, the goal of image registration is to align images of same scene by removal of the potential geometrical distortion existed.
数码相机摄入图像时产生的线性几何畸变会给叶片面积的测量带来误差。
The leaf image taken by digital camera often has linear geometric distortion, it can bring error to the measurement of leaf area.
对于视觉系统来说，影响精度的因素主要有图像噪声、系统分辨率和几何畸变。
As far as a vision system is concerned, its accuracy is affected mainly by noise, system resolution and geometrical distortion.
由于利用了折射率信息，投影折射率计算机层析图像消除了常规光学相干层析图像中的几何畸变。
Meanwhile, due to the utilization of information on refractive index distribution, geometrical distortions usually observed in conventional OCT images are avoided in the PICT image.
在机器视觉应用中，摄像机光学系统产生的图像存在不同程度的几何畸变。
In the machine vision application, geometrical distortion in optical lens imaging is inevitable.
准确地测量含有广角耦合物镜的图像显示系统的几何畸变是实现图像显示系统几何畸变数字校正的前提和关键。
Accurate measurement of geometrical distortion of image display system with wide-angle objective lens is the precondition and key of digital distortion correction.
图像配准的目的是建立两幅图像间的几何变换关系，去除或减小两幅图像的几何畸变，从而实现图像的几何校正。
The major purpose of registration is to establish geometric transformation between two images, and remove or suppress the geometric distortions between them.
考虑到在非常小的平面内，图像的空间畸变可看成是图像的几何线性变化。
In consideration of within the tiny scope, the spatial distortion of the image can be seen as a line change of the space.
将获得的图像分为不同大小的5个视窗范围，测量直接扫描图像和DRR图像的边界绝对位置误差并计算其几何畸变率。
Geometric distortion in 5 different fields of view (FOV) was calculated by measuring the absolute borderline error of direct scan and coronal digital reconstructed radiograph (DRR).
为此，论文提出了一种基于网格图像的几何校正算法，该算法运用分段插值的思想，利用分块低次插值去逼近几何畸变，达到高次插值的效果。
Therefore the authors put forward a calibration algorithm based on the grid image, which USES subdivision interpolation to approach the deformation and get a result of high-order interpolation.
然而一般的X射线仪采集到的图像都存在几何畸变，引起分析过程中的误差，因此有必要对从X射线仪获取的图像进行几何校正。
So it is necessary to correct such images. In this paper I consider the causes of the geometric distortion in X-ray images, and apply the camera calibration into the X-ray image correction.
同一幅图像内不同条带的成像时间不一致，将会导致条带间的几何错位，即地球自转引起的几何畸变。
Then different belts in the same image have the different imaging time, making the geometric dislocation within different belts, which is also named as deformation caused by the earth-rotation.
同一幅图像内不同条带的成像时间不一致，将会导致条带间的几何错位，即地球自转引起的几何畸变。
Then different belts in the same image have the different imaging time, making the geometric dislocation within different belts, which is also named as deformation caused by the earth-rotation.
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