Otsu2d_Xiefen

说明：  基于ADMM的TV正则化最小化稀疏重建算法(TV Minimization Sparse Reconstruction Algorithms Based on ADMM)

CloudViewer （点云可视化软件）是一款面向三维点云数据处理、点云开发专业人员及爱好者的三维点云可视化软件，该软件具备以下功能： 点云 IO。常见点云格式文件（如 ply, pcd 和 obj）的读取、保存、删除清空及可视化。 点云的基本操作。设置点云颜色，合并多个点云，点云格式转换等。 可视化的基本操作。设置视图窗口背景颜色，设置点云的可视化尺寸，设置坐标系的显示，设置视图方位（Main view, Left view, Top view），网格面片显示，网格线框显示等。 模型生成。生成简单立方排布的点云以及基本三维模型，该功能主要用于测试。(CloudViewer uFF08 u70B9 u4E9 u53E u53E3 u5316 u8F6 u4EF0 u0EF6 uFF09 u662F u4E00 u6B3E u972 U4E91 u53E1 u53E1 u3EF3 u7EF3 u7EF3 u3EF0 U5177 u5907 u4E0 u4E0B u529F u80FD uFF1A u70B9 u4E91 i u3002 u5E3 u8C1 u70B9 u4E91 u683C u5F0F u6587 u4EF6 uFF08 u5982 ply, pcd u548C obj uFF09 u7684 U8BFB u53D6 u3001 u4FDD u5B58 u3001 u5220 u9664 u6E05 u7A7A u53CA u53EF u89C6 u5316 u3002 u70B9 u4E9 u7E9 u7684 u57FA u672C u64CD u4F5C u3002 u8BBE u7F6E U70B9 u989 u989.0 u8279 u03109 u03H2 U672C u64CD u4F5C u3002 u8BBE u7F6E u89C6 u56FE u7A97 u53E3 u803 u803 UFF0C u8BBE u7F6E u5750 u6807 u7CFB u7684 u663E u793A uFF0C u8BBE u7F6E u89C6 u56FE u 65B9 u4F4D uFF08Main view, Left view, Top view uFF09 uFF0C u7F51 u683C u973 u7327 u733A u739A u7R3 U751F u6210 u3002 u751F u6210 u7B80 u5355 u7ACB u659B u6392 u5E03 u7E09 U80B0 u4E3B u8981 u7528 u4E8E u6D4B u8BD5 u3002)

说明：  sprites communication to windows in unix sys

lda线性特征提取，用于人脸识别，首先进行小波特征提取后用lda提取特征。(lda linear feature extraction for face recognition, first of all, after feature extraction using wavelet feature extraction using lda.)

说明：  实现多视两幅图像的surf特征点提取与匹配(Achieve surf feature point extraction and matching of two multi-view images)

说明：  matlab图像滤波去噪分析及其应用 双线性滤波、Kirsch滤波、超限邻域滤波、逆滤波、双边滤波、同态滤波、小波滤波、六抽头滤波、约束最小平方滤波、非线性复扩散滤波、Lee滤波、Gabor滤波，、Wiener滤波、Kuwahara滤波、Beltrami流滤波、Lucy?Richardson滤波、NoLocalMeans滤波等研究内容。(Analysis and application of MATLAB image filtering and denoising Bilinear filtering, Kirsch filtering, transfinite neighborhood filtering, inverse filtering, bilateral filtering, homomorphic filtering, wavelet filtering, six tap filtering, constrained least square filtering, nonlinear complex diffusion filtering, Lee filtering, Gabor filtering, Wiener filtering, Kuwahara filtering, Beltrami flow filtering, Lucy Richardson filtering, nolocalmeans filtering, etc.)

说明：  对不同噪声强度的加噪图像，用粒子群优化算法优化结构元素，找到最优结构元素，对图像进行去噪操作，使恢复的图像达到最大峰值信噪比(Noise intensity for different images with noise, using particle swarm optimization algorithm to optimize the structure elements, find the optimal structure element, the image de-noising operation to restore the image to a peak signal to noise ratio)

基于小波包变换的去噪处理 全局阈值去噪图像 对图像进行平滑处理以增强去噪效果（中值滤波）(Denoising based on wavelet packet transform global threshold denoising image smoothing image enhancement and denoising effect (median filtering))

本设计主要目的是实现染色体的识别计数。首先对图形进行预处理，将图像转化为灰度图、滤波去噪，然后对图像进行二值化，合理选取阈值，进行直方图阈值处理，最后去除图像中面积过小的杂点，最终实现识别计数。(The main purpose of this design is to achieve the identification of chromosome count. The graphical pre-processing, the image is converted to grayscale, filtering denoising, image binarization, reasonable selection of the threshold, histogram thresholding, and finally remove the image area is too small noise , and ultimately identify the count.)

通过统计直方图并作相应的处理完成了红外图像非均匀性校正，特别是针对红外图像中常见的条纹非均匀性(Through statistical histogram and processed accordingly infrared image non-uniformity correction, especially for the common striped infrared image non-uniformity)