It achieves space chaos of an image with a pseudo-random sequence generated by linear congruential method, and achieves gray chaos by XOR operation.
该算法利用线性同余模型产生伪随机序列以对图像进行空间置乱，采用异或操作实现图像的灰度变换。
Once the interest points were detected, the image matching process in an image sequence is performed using local gray-value differential invariants.
在获得了兴趣点之后，利用兴趣点处的局部灰度差分不变量进行序列图像的点特征匹配。
Then the location of target's frame is solved by the pixel gray statistical changes in the vast image sequence.
进而，从图像像素灰度这一最基本的要素着手，研究了如何在大量的序列图像中搜索目标帧位置的方法。
Firstly, we detect the points of interest in a pair of images in image sequence, and then match the point set using the gray value differential invariants.
首先在序列的两幅图像中检测兴趣点，并运用灰度差分不变量进行两点集之间的匹配。
For traditional watermarking techniques based on fractal coding, watermarking format is limited to binary sequence 0, 1, thus incapable of gray-scale image embedding.
传统的基于分形编码的水印技术一般嵌入0,1序列，没有实现灰度图像嵌入。
By using image difference and gray stretch technique, image histogram analysis method was used to investigate the movement of particles in image sequence.
利用差分法配合灰度拉伸对颗粒运动图像进行了处理 ，直方图分析表明 ，像素在灰度上的分布特性与颗粒运动的剧烈程度具有相关性。
By using image difference and gray stretch technique, image histogram analysis method was used to investigate the movement of particles in image sequence.
利用差分法配合灰度拉伸对颗粒运动图像进行了处理 ，直方图分析表明 ，像素在灰度上的分布特性与颗粒运动的剧烈程度具有相关性。
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