mean-shift

采用Zernike多项式拟合横向剪切干涉，得出三幅图像。(Using Zernike polynomial fitting lateral shearing interferometer, draw three images.)

该软件可实现传统的NMF非负矩阵分解，希望能对大家有所帮助！(This software can realize non-negative matrix factorization. I hope it is helpful for all of us.)

建立几何畸变模型，实现畸变矫正。通过获取模板图像的坐标值，求取变换模型参数，求出变换多项式的系数。(IMAGECORRECT M-file for imageCorrect.fig IMAGECORRECT, by itself, creates a new IMAGECORRECT or raises the existing singleton*.)

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)

石墨烯的结构编程图,这是费了工作人员很长时间才完成的。(The figures of graphene)

基于silvaco平台 利用athena软件做的一款PN结二极管的二维图仿真(Based on silvaco platform using the officer software do a 2 d figure of the p-n junction diode simulation)

三维三自由度机器人仿真程序，可以离线编程(3D three-robot simulation program can OLP)

制作二维地形,并且可以对其添加线表示，还可以做分层设色二维地形图图(Dimensional terrain)

基于谱间预测的高光谱图像压缩。单向预测与双向预测，以及参考波段的最优选取(The prediction based on spectral hyperspectral image compression. One-way bi-directional prediction and prediction, as well as reference to select the optimal band)

最大类间方差法是由日本学者大津(Nobuyuki Otsu)于1979年提出的,是一种自适应的阈值确定的方法,又叫大津法,简称OTSU。它是按图像的灰度特性,将图像分成背景和目标2部分。背景和目标之间的类间方差越大,说明构成图像的2部分的差别越大,当部分目标错分为背景或部分背景错分为目标都会导致2部分差别变小。(The largest between-class variance method was proposed by the Japanese scholar Nobuyuki Otsu in 1979. It is an adaptive threshold determination method, also known as the Otsu method, or OTSU for short. It is based on the grayscale characteristics of the image and divides the image into two parts: the background and the target. The larger the variance between the background and the target, the greater the difference between the two parts that make up the image. When the part of the target is wrongly divided into backgrounds or part of the background is mis-divided into goals, the difference between the two parts will be smaller.)