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MATLAB实现基于互相关的亚像素级图像匹配/配准源代码Free Source Code for Efficient subpixel image registration by cross-correlation
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MATLAB实现基于互相关的亚像素级图像配准源代码,MATLAB_Image_subpixel_Mutual_info,MATLAB-based cross-correlation of sub-pixel image registration source code, MATLAB_Image_subpixel_Mutual_info
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SAR图像和光学图像的配准算法！！！matlab源码~~~~绝对好！-SAR image and optical image registration algorithm! ! ! matlab source code ~ ~ ~ ~ definitely good!
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这是基于相位相关法的图像配准拼接 MATLAB源程序-This is based on phase correlation image registration mosaic MATLAB source code
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The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all
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关于图像配准中的交叉相关的实现，可以直接调用。-About Image Registration in the implementation of cross-correlation can be directly called.
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此程序是基于互相关的亚像素级图像匹配的代码，此种配准方法误差小，精度高。-This procedure is based on the cross-correlation of sub-pixel image matching of the code, such a registration method error and high accuracy.
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正交相关目标检测，采用正交相关方法检测指定目标在图像中的位置。适用于计算机视觉中的视频目标检测、视觉目标检测、目标定位、视觉目标跟踪、视频目标跟踪、图像匹配、图像配准等工作。-cross relation detection is used to detect object in image for in the field computer vision such as visual object detection, motion detection, object localization
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别对两幅图像提取广义特征点后，
利用基于根均方误差和交叉相关的两级匹配算法完成同名控制点的建立。并以局部加权直线拟合方法来校正图像
的几何畸变。最后建立两幅图像之间的函数映射关系，完成图像的配准。实验结果证明了该方法的有效性。-Do the two images extracted generalized characteristic point, the root mean square error-based and cross-correlation of the two cont
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寻找相应的特点,在一副图片的基础是许多光流、立体的视觉和图像间的配准算法。一个发现匹配方法简单,取一小块一个图像,计算其滑动互相与其他的形象,并找到一个高峰。此表格提供了课堂的实现方法。-Finding corresponding features in a pair of images is the basis of many optic flow, stereo vision and image registration algorithms. One straightforward app
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Efficient subpixel image registration by crosscorrelation. This code gives the same precision as the FFT upsampled cross correlation in a small fraction of the computation time and with reduced memory requirements
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图像配准：选择图像需要配准的子区域，计算归一化互相关，通过它来确定图像配准的区域，显示配准后的图像。-Image registration: Select the image sub-region need registration, computing normalized cross-correlation, by which to determine the image registration area to show the image after registration.
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图像配准主要是通过两幅图像之间的互相关的最大值来确定配准的位置，从而在另一幅图像中显示配准的图像-Image registration is mainly through the maximum cross-correlation between the two images to determine the location of the registration and registered image, which is displayed in another image
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基于NCC（归一化互相关）的图像配准算法-NCC (normalized cross-correlation), image registration algorithm
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图像配准，利用互相关的方法进行图像配准，能达到良好效果-Image registration, the use of the cross-correlation method of image registration, and can achieve good results
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MATLAB实现基于互相关的亚像素级图像匹配/配准源代码，可以使用-MATLAB based on the cross-correlation of sub-pixel image matching/registration source code, you can use
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归一化互相关图像配准
有时一幅图像是另一幅图像的子图像，可以利用归一化互相关通过移动一幅图像来是两幅图像配准。-Normalized cross-correlation image registration Sometimes an image is another image of the sub-image, by moving an image using the normalized cross-correlation to two image registration.
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采用matlab语言对两幅图像采用互相关函数进行图像配准-Two images using matlab language pairs using cross-correlation function for image registration
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图像拼接(image mosaic)技术是将一组相互间重叠部分的图像序列进行空间匹配对准,经重采样合成后形成一幅包含各图像序列信息的宽视角场景的、完整的、高清晰的新图像的技术。图像拼接在摄影测量学、计算机视觉、遥感图像处理、医学图像分析、计算机图形学等领域有着广泛的应用价值。 一般来说,图像拼接的过程由图像获取,图像配准,图像合成三步骤组成,其中图像配准是整个图像拼接的基础。本文研究了两种图像配准算法:基于特征和基于变换域的图像配准算法。 在基于特征的配准算法的基础上,提出一种稳健的基于特征点的
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基于互相关的图像配准程序，能实现亚像素级的配准-Sub pixel level registration based on cross correlation image registration process
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