On the basis of image cross correlation of brightness distribution and Fast Fourier Translation, the implementation of Particle image Velocimetry was studied.
在图像灰度分布互相关和快速傅立叶变换的基础上，研究了粒子图像测速的实现。
A new stitching algorithm based on area feature and cross correlation for the sequence image with transformation of translation, rotation and isometric scaling is proposed.
针对具有平移、旋转、缩放变换的序列图像连续拼接，提出一种将基于区域特征的配准算法和基于灰度交叉相关的配准算法相结合的拼接算法。
A new method for image sequence object detection based on the normalized cross correlation coefficient is proposed.
提出一种基于归一化互相关系数的图像序列运动目标检测方法。
Analyze the time correlation in the differential image sequences: Use cross-entropy to track the blocks of image sequences according to the time correlation along the time axes.
分析差分图像序列沿时间轴的时间相关性：利用交叉熵来分析差分图像序列的时间相关性，根据图像序列的时间相关性来跟踪各差分图像序列块。
A cross correlation algorithm based on Fast Fourier Transform (FFT) for Digital Particle Image Velocimetry (DPIV) is discussed thoroughly in this paper.
详细研究了基于快速傅立叶变换(FFT)的数字粒子图象测速技术(DPIV)的互相关算法。
The particle image velocimetry (PIV) is an effective and non-intrusive technique to measure the planar distribution of velocity in the fluid based on the cross-correlation of flow images.
PIV技术是一种基于流场图像互相关分析的二维流场非接触式测试技术。
The particle image velocimetry (PIV) is an effective and non-intrusive technique to measure the planar distribution of velocity in the fluid based on the cross-correlation of flow images.
PIV技术是一种基于流场图像互相关分析的二维流场非接触式测试技术。
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