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归一化互相关高速计算模块OpenCV工程,Daniel Eaton已将其生成Matlat Mex文件,比Matlab内建的normxcorr2()效率要高-Normalized cross-correlation of high-speed calculation module, Daniel Eaton have been wrapped the C++ code from OpenCV project and making it available as a MATLAB MEX-file,
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matlab 图像处理 显示两幅子图像的归一化互相关-matlab image processing show that two sub-image of normalized cross-correlation
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The normalized cross correlation (NCC) implemented in C++ and Matlab
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ZNCC Normalized cross correlation
m = zncc(w1, w2)
Compute the zero-mean normalized cross-correlation between the two
equally sized image patches w1 and w2. Result is in the range -1 to 1, with
1 indicating identical pixel patterns.-ZN
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本程序展示了二维归一化互相关用于
模式匹配和目标跟踪。该程序提示用户选择感兴趣的（ROI）的区域和对同类目标进行跟踪的数目。归一化交叉相关图表明，当值已超过设定的阈值时目标就已经确定。-This demo illustrates the use of 2-D normalized cross-correlation for
pattern matching and target tracking. The demo prompts the user to select
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实现了三种图像匹配算法
1：归一化互相关匹配算法
2：基于Hausdorff距离的图像匹配算法
3：图像不变矩匹配算法-Three matching algorithms to achieve 1: normalized cross correlation matching algorithm 2: the image matching algorithm based on Hausdorff Distance 3: Image Invariant Moment Matchin
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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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快速NCC(normalized cross correlation)算法实现。比matlab自带的normxcorr2 快10-20倍。在windows Unix环境下c语言，和matlab编译通过。-use Matlab on Windows or Linux or Mac OS X and want fast (exact, general-case) normalized correlation (NCC) code right now, then download it and enj
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The code is there for the Normalized Cross Correlation Techniques for object detection in matlab
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【谷速软件】matlab有偿编程 对于灰度和彩色图像的快速归一化互相关 可以作为参考使用-[Valley] matlab paid programming speed software for grayscale and color images quickly normalized cross-correlation can be used as a reference
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此程序是在MATLAB下进行图像配准，通过归一化互相关来确定图像配准的区域。-This program is for image registration in MATLAB, by normalized cross-correlation to determine the image registration area.
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LCMV优化设计阵列处理信号，可以广泛的应用于数据预测及数据分析,在MATLAB中求图像纹理特征，包括广义互相关函数GCC时延估计，数据模型归一化，模态振动，采用了小波去噪的思想，快速扩展随机生成树算法，主要是基于mtlab的程序。- LCMV optimization design array signal processing, Can be widely used in data analysis and forecast data, In the MATLAB image texture
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Including the generalized cross-correlation function GCC time delay estimation, Normalized data model, modal vibration, Using MATLAB dynamic clustering or iterative self-organizing data analysis.
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