filter

说明：  实现图像的分形编码，还有一些其他的编码 此效果较好，推荐(Fractal image coding, there are other encoding this better is recommended)

利用棋盘格对相机进行标定，加入误差判定及像素空间到实际物理空间的距离计算(The camera is calibrated by checkerboard, the error is added and the distance of pixel space to the actual physical space is calculated)

利用CFAR检测SAR图像中的高亮度目标。(Detect targets with high backscattering intensities by CFAR algorithm)

说明：  图形图像出来库文件，挺好用的，直接包含进工程就可以用了 (Graphic images from the library files, good used directly included into the project can be used)

分为行人检测，特征提取，行人跟踪三步，其中最主要的代码也是这个代码的中心是行人跟踪这一部分，我采用的是kalman滤波器进行行人下一帧位置的预测，可以很好的提高行人跟踪的鲁棒性，跟踪结果比较准确。(Into pedestrian detection, feature extraction, pedestrian tracking steps, which is the center of the main code of the code is pedestrian tracking this section, I kalman filter is used to predict the position of the next frame pedestrians, can be a good improvement pedestrian tracking robustness, tracking results more accurate.)

matlab环境下的基于形态学的分水岭算法,进行图象分割.(Matlab environment based on the morphology of the watershed algorithm for image segmentation.)

online random forest 在线随机森林做跟踪和分类的代码(online random forest-line tracking and classification random forest to do the code)

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.)

图像恢复：快速非局部均值滤波--A.Buwades等人提出的非局部均值滤波的快速实现方法(Image restoration: the proposed fast non-local means filtering- A. Buwades non-local means filtering Fast Implementation)

说明：  用水平集做的 MRI大脑图像分割 内有用法说明 （带图形界面）(To do with the level set within the MRI brain image segmentation instructions (with graphical interface))