huoquzhidingyanse

一段很好的图像放大缩小程序，解码后可以运行。(A very good image magnification narrow program, the decoding can run. )

画图源程序-图像数据显示，可以对数据进行时间轴的曲线显示，也可以是空间轴的多维显示 (Paint source- the image data, the data can be curve shows the time axis, it can be multi-dimensional display space axis)

用kinect获取3D图像，并从3D图像中用OpenCV方法从深度图中获取梯度边缘，从而得到更多信息，做后续处理(Get with kinect 3D images, and get the edge gradient from the depth map from a 3D image using OpenCV method to obtain more information, do follow-up treatment)

偏微分方程_PDEs_模型在图像处理中的若干应用(_PDEs_ Model partial differential equations in image processing)

将传统的串行均值滤波算法修改为基于CUDA的并行的滤波算法(Modify traditional serial mean filtering algorithm based on CUDA parallel filtering algorithm)

说明：  直方图均衡化的一个程序，只能对BMP的黑白图像进行直方图均衡化(A histogram equalization process, only black and white BMP image histogram equalization)

基于侧抑制的图像分割，在ＰＣＮＮ分割的基础上实现(failed to translate)

实现人体跟踪的源代码，实现对视频中目标人物的跟踪(The human tracking source code, the video of the target person tracking)

基于规格化Hough变换的天波超视距雷达检测前跟踪算法(Hough transform based on the normalized horizon radar detection and tracking algorithms)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)