Description: 结合分级关键点互相关迭代法与搜索空间标记法,设计了一种有效提高图像相关匹配速度的算法。
其中分级关键点互相关迭代算法快速实现了由重要到不重要像素点的逐级迭代互相关匹配计算,算法在保
证精度的前提下可以随时结束并输出相关匹配值 而搜索空间标记法则能快速排除掉大量参数空间内不可
能匹配的点。实验证明,这种算法能在正确配准图像的前提下大幅度提高匹配速度。-Combination of inter-related key points classification iteration method with search space notation, design an effective image correlation matching to improve the speed of the algorithm. One key point grading iterative cross-correlation algorithm for the rapid realization of important to unimportant by pixel matching level iterative cross-correlation calculation, the algorithm to ensure the accuracy of the premise can be concluded and the output value of the relevant search space matching tag rules quickly ruled out a large number of parameter space points can not be matched. Experiments show that this algorithm can be in the right image registration under the premise of substantial increase in matching speed. Platform: |
Size: 99328 |
Author:李锋 |
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Description: 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 the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.-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 the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented. Platform: |
Size: 98304 |
Author:Harry |
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Description: 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. Platform: |
Size: 5120 |
Author:sai |
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Description: 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. Platform: |
Size: 274432 |
Author:张 |
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Description: 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, visual object tracking, video object tracking, image registration, image matching, etc. Platform: |
Size: 109568 |
Author:朱亮亮 |
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Description: 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 control point matching algorithm to complete the establishment of the same name. And to locally weighted linear fitting method to correct image distortion. Finally set up between the two images, the mapping function to complete the image registration. Experimental results show the effectiveness of the method. Platform: |
Size: 568320 |
Author:林杰 |
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Description: 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 approach to finding a match is to take a small patch of one image, compute its sliding cross-correlation with the other image, and find a peak. This submission supplies a class which implements this method.
Platform: |
Size: 776192 |
Author:王羽 |
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Description: 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 Platform: |
Size: 3072 |
Author:hamraz |
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Description: 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. Platform: |
Size: 1024 |
Author:shubin |
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Description: 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 Platform: |
Size: 4096 |
Author:赵建平 |
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Description: 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 Platform: |
Size: 665600 |
Author:yx |
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Description: 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 Platform: |
Size: 273408 |
Author:席pudn |
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Description: 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配准算法。首先改进Harris角点检测算法,有效提高所提取特征点的速度和精度。然后利用相似测度NCC(normalized cross correlation——归一化互相关),通过用双向最大相关系数匹配的方法提取出初始特征点对,用随机采样法RANSAC(Random Sample Consensus)剔除伪特征点对,实现特征点对的精确匹配。最后用正确的特征点匹配对实现图像的配准。本文提出的算法适应性较强,在重复性纹理、旋转角度比较大等较难自动匹配场合下仍可以准确实现图像配准。-Image mosaic is a technology that carries on the spatial matching to a series of image which are overlapped with each other, and finally builds a seamless and high quality image which has high resolution and big eyeshot. Image mosaic has widely applications in the fields of photogrammetry, computer vision, remote sensing image processing, medical image analysis, computer graphic and so on. 。In general, the process of image mosaic by the image acquisition, image registration, image synthesis of three steps, one of image registration are the basis of the entire image mosaic. In this paper, two image registration algorithm: Based on the characteristics and transform domain-based image registration algorithm. In feature-based registration algorithm based on a robust feature-based registration algorithm points. First of all, to improve the Harris corner detection algorithm, effectively improve the extraction of feature points of the speed and accuracy. And the use of a similar measure of NC Platform: |
Size: 332800 |
Author:刘 |
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Search - image registration cross correlation