pipeidian_eight

视频中运动的放大，欧拉运动放大,包含事例视频，以及代码使用方法。(Enlarge video motion, Euler motion amplification, contains examples of video, as well as the code to use.)

利用已有真实场景色彩图片转换为灰度图像，实现SAR成像处理，(Use of existing real scene color images converted to grayscale images, the SAR imaging processing)

一套关于matlab角点提取的程序，可用于张正友传统的相机标定法提取棋盘格角点。(Set matlab corner extraction procedures, Zhang Zhengyou traditional camera calibration method can be used to extract the checkerboard corners.)

本代码实现的功能：24位位图（BMP）的灰度化。具体实现过程：打开图像文件,找到数据区(每3个字节代表一个象素的R,G,B值),循环扫描,根据RGB的权重,计算出灰度值. 如果要得到256色灰度图,则要新建一个调色板,修改信息头和文件头.(Realize the function of the code: 24-bit bitmap (BMP) of the gray. Concrete realization of the process: Open the image file, locate the data area (3 bytes per pixel on behalf of a R, G, B values), cyclic scan, according to the weight of RGB, gray value is calculated. If you want to get 256 colors grayscale, would create a new palette, modify the header and file header information.)

该程序利用MATLAB语言计算本质矩阵和基本矩阵，其中有用到ransac八点算法(The program utilizes the MATLAB language, the nature of the matrix and the fundamental matrix, which is useful to ransac eight algorithms)

说明：  基于matlab的简单手势识别代码，初学者可自行下载。(Simple gesture recognition code, beginners can download.)

说明：  用matlab写的t-snake的算法用于处理比较简单的情况有一定参考价值(using Matlab write t-snake algorithm for handling simple reference of a certain value)

通过Matlab实现维纳滤波及逆滤波复原图像 不使用matlab自带函数 而是自编程序实现(Matlab to achieve through the Wiener filter and inverse filter to recover the image does not use function but rather bring their own self-matlab program)

Downsample image console program using opencv.

该代码用于实现视觉目标跟踪研究中的协方差跟踪，能够把多种时空特征融合于统一的模型中，在实现视觉目标跟踪时具有较好的鲁棒性，而且其维数等于使用特征的数量，与各个特征的维数无关，因此，其计算复杂度较小，实时性较好。(This code is used to realize covariance tracking in the research field of visual object tracking of computer vision. It enables fusion of various spatial-temporal features into an unified object feature model, hence robust to noise. In addition, its dimension is equal to the number of the features used and independent of the dimensions of the features used. Therefore, it satisfies the requirement of real-time application due to its low computation cost.)