Gaussian-Noise-Image-Add

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,)

说明：  使用Matlab实现遥感时空融合算法STARFM(A code for spatio-temporal fusion (STARFM))

K-D树源码，不错的空间查找算法，在三维重建和匹配中应用较多！(KD tree source, good space search algorithm, in the three-dimensional reconstruction and matching the application of more!)

vc编的运动检测系统，特征区域匹配，运动物体提取，运动轨迹跟踪等(vc series of the motion detection system, features regional match, moving objects extraction, trajectory tracking)

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)

说明：  一种改进的活动轮廓模型－－GVF梯度矢量流模型(Improved active contour models-- gradient vector flow gradient vector model)

该程序为立体空间内艾里光束的传播，使用角谱传播定理(The program for the Airy beam propagation in three-dimensional space, using the angular spectrum propagation theorem)

字符权的匹配的算法 The algorithm for matching character rights(Right of character matching algorithm, The algorithm for matching character does)

说明：  该程序SSIM（结构相似度）是基于matlab平台，用于图像的客观质量评价，是FR中精典算法(The program SSIM (structural similarity) is based on matlab platform for an objective image quality evaluation, is in the classical algorithm FR)

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)