Capture from TWAIN source image, convert and print.
捕获的twain源图像，转换和打印。
This is the source image from Fallen Lights contest.
这是比赛的堕落灯源图像。
The image below shows the source image with all above corrections made.
以下是进行了上述修改的原始图。
The font specified is included in the code archive, and the source image is 300dpi.
指定的字体附带在代码归档中，并且源图像为300dpi。
If the shift value is zero, the VI USES the specified bit depth of the source image.
如果移位值为零(N为0)，则函数使用源图像的指定位深度。
Are there good and bad colour gradients in source image data that produce larger output?
有好的和坏的颜色渐变的源图像数据，产生更大的输出？
Hook in open source image recognition software for automatically suggestion image regions to tag.
挂入开源图片识别软件以自动给出标记的区域。
The VI shifts the source value to the 8-bit range using the specified bit depth of the source image.
函数使用指定位深度的源图像将其源值转换为8位范围。
The VI shifts the source value to the 8-bit range using the specified bit depth of the source image.
该函数使用指定位深度源图像将其源值转换为8位范围（应该是每个颜色分量都使用右移，NI没有介绍）。
Multi-source Image Fusion is an important and useful subject in Image Understanding and Computer Vision.
多源图像融合是图像理解和计算机视觉领域中的一项重要课题。
If the source image has a specified bit depth, the VI USES the bit depth when performing this conversion.
如果源图像具有指定的位深度，该函数使用指定位深度在执行此转换时。
Now you need to determine the destination disk for restoration and the disk where the source image is to be placed.
现在，需要为恢复确定一个目标磁盘以及将要放置源映像的磁盘。
Experimental results are obtained based on analysis and discussion of practical applications of multi-source image fusion.
对多源卫星遥感影像融合的具体应用进行了分析和讨论，给出了部分实验结果。
From human visual features, this measure evaluates the similarity of the gradient fields between the source image and fused image.
从人的视觉特性，这一措施评估的梯度场的源图像和融合图像之间的相似度。
Our system allows users to adjust the interpolation weights between source image and target image to control the similarity degree.
系统允许用户调整权重，在源五官与目标五官间进行插值，控制结果图像与二者的相似程度。
The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel.
该函数执行高斯金字塔结构下采样的步骤。首先，它与内核的源图像进行卷积。
We advance the method of fusion processing for the source image in order that we can get more accurate image before the image processing.
为了在图像处理之前得到更精确的图像，我们提出了对多源图像进行融合处理的方法。
Zhendui different field of multi-source image fusion, we propose a affine transformation and linear combination of registration methods;
针对不同视场的多源图像融合技术，本文提出一种仿射变换和线性插值相结合的配准方法；
It depends on completely the given two source image and produces the good morphing image, without specifying the feature marker beforehand.
该方法完全依赖于给定的两幅源图像，自动生成期间的变形图像，不需要预先在两幅源图像中指定特征基元。
Furthermore, the size of the sub-image in wavelet domain is a quarter of the source image, so the convergence speed of our method is faster.
此外，高频子图像的大小为原图的四分之一，因此计算量大大减少，算法的收敛速度更快。
First of all, this dissertation researches the existing multi-source image registration techniques and introduces the principles of analysis.
首先，本文对现有的多源图像配准技术进行原理上的分析与介绍。
The property of input image is used to construct source image pair. We learn the relationship between source image pair, and then, use it for segmentation.
论文利用图像自身的特性构造训练集合的源图像对，并学习他们之间的关系，达到图像分割的目的。
Greyscale image colorizing is a method that transplants color characteristics from a colorized image, source image to a greyscale image, destination image.
灰度图像的彩色化是将一幅彩色图像的颜色特征传递给另一幅灰度图像，使灰度目标图像具有与源彩色图像相似的颜色。
This paper proposed an evaluation model for different source image fusion quality to resolve the limitations of single factor index on the quality evaluation.
针对单一因素指标对图像融合质量评价的局限性，提出了一种异源图像融合质量评价模型。
With the use of multi-sensor (multiple images), the fused image contains a more complete and accurate description of the scene than any of the individual source image.
由于利用了来自多传感器的多源图像，所以，融合后图像对场景的描述比任何单一源图像都更全面、更精确。
After giving the definition of multi-source image registration, the common image registration methods are divided into two types: intensity-based methods and feature-based methods.
在给出多源图像配准的定义后，将常见的图像配准方法分为基于图像灰度的方法和基于图像特征的方法两大类。
According to local gradient information, wavelet low frequency coefficient obtained from source image is selected to compose respective low frequency coefficient of the fused image.
根据局部梯度信息对源图像的小波低频系数进行选择，获取融合图像的对应低频系数。
In trapezoid correction, the obtained RGB data was calculated by trapezoid transforming after image interpolation, and made the contrast of the trapezoid image with the source image.
在梯形校正中，将所得到的RGB图像数据进行插值运算后再进行梯形转换，并将得到的梯形图形与原图形进行了对比。
Initially, the selecting of source image of the color night vision system was done by manual work which took quite a long time with poor accuracy and restricted the automation efficiency.
而最初，夜视图像彩色化对源图的选择过程是根据夜视图像的特点依靠人工选择完成的，耗时颇多且难以准确，制约了系统的自动化程度。
Initially, the selecting of source image of the color night vision system was done by manual work which took quite a long time with poor accuracy and restricted the automation efficiency.
而最初，夜视图像彩色化对源图的选择过程是根据夜视图像的特点依靠人工选择完成的，耗时颇多且难以准确，制约了系统的自动化程度。
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