SBS-Threshold

根据教程自己写的对单目相机的标定，以及对一张镜像畸变图像的矫正，最后进行二维测距，根据像素值测得一条平面曲线的真实长度(According tutorials to write their own right monocular camera calibration, as well as a mirror image distortion correction, the final two-dimensional distance, measured according to the pixel values ​ ​ of the true length of a plane curve)

实现文献‘Fast and robust multiframe super resolution’的仿真，程序中有详细解释，对于多帧超分辨率重建中，退化图像获得，如何进行迭代重建，都有详细说明。(Literature ' Fast and robust multiframe super resolution' simulation, the program explained in detail for the multi-frame super-resolution reconstruction of the degraded image, iterative reconstruction, there are detailed instructions.)

水平集最新算法，基于局部能量函数，并做了改进(level set new Algorithm, make some improve)

对像素点较少的原始图像进行二维插值，以提高图像的平滑度。()

说明：  snakes算法简单程序（可运行），对初步学习snakes算法有帮助(snakes simple algorithm (runs), the preliminary study snakes help Algorithm)

使用结构光投射，用PMP的相位测量方法进行三维测量(Using the structural light projection, the method of measurement of PMP is measured in three dimensions)

四邻域加权平均算法 实现了图像的高质量重采样(resample function)

一个比例和方向自适应均值漂移跟踪算法（SOAMST） 提出本文所要解决的问题，如何估计的规模和方向 改变均值漂移下的目标跟踪框架。在原来的均值偏移 跟踪算法，可以很好地估计目标的位置，规模的同时， 方向的变化，不能自适应估计。考虑到图像（重量） 是来自于目标运动模型和候选模型可以代表的可能性，一个 像素属于目标，我们证明了原来的均值漂移跟踪算法可以 推导出的重量图像的零阶和一阶矩。随着零阶 矩和目标模型和候选模型之间的Bhattacharyya系数， 提出了简单而有效的方法来估计的规模为目标。然后一种方法， 利用估计的区域和第二阶中心矩，提出 自适应地估计目标的宽度，高度和方向的变化。广泛 实验来证实所提出的方法，并验证其可靠性 规模和方向变化的目标。(A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this paper to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, while the scale and orientation changes can not be adaptively estimated. Considering that the weight image derived from the target model and the candidate model can represent the possibility that a pixel belongs to the target, we show that the original mean shift tracking algorithm can be derived using the zeroth and the first order moments of the weight image. With the zeroth order moment and the Bhattacharyya coefficient between the target model and candidate model, a simple and effective method is proposed to estimate the scale of target. Then an approach, which utilizes the estimated area and the second order center moment, is proposed to adaptively e)

说明：  经典高斯烟雨模型、固定点源、3d模拟、三维图、等高线图(Classic Gaussian smoke and rain model, fixed point source, 3d simulation, three-dimensional map, contour map)

著名的FastICA 算法,最新版本,修改了以前的Bug,(The famous FastICA algorithm, the latest version, modify the Bug before,)