deedsMIND

说明：  SLM与PTS的融合(低复杂度),直接可运行(SLM and PTS)

利用数字图像处理软件halocn来进行缺陷检测，附带有之前做工程项目的原图源代码，还有ppt和实验指导书(Halocn using digital image processing software for defect detection, comes with artwork done before the project source code, as well as ppt and experimental instructions)

基于IBP算法的超分辨率图像重建，原理讲得十分清楚。(Super-resolution image reconstruction based on IBP algorithm. The principle is very clear.)

将一幅图像以jpeg的形式进行压缩，对初学者很有用(Jpeg form of an image compression, very useful for beginners)

用Kalman滤波方法对单个目标航迹进行预测，并借助于Matlab仿真工具，对实验的效果进行评估。 (Target tracking application context radar data processing, radar in the search to the goals and objectives of the record location data of the measured target position data (called a point of the track) processing, automatic formation flight path, and the target in the next The moment the position prediction. This article briefly discusses the Kalman filtering method to predict a single target track, and by means of Matlab simulation tools to assess the effect of the experimental.)

isar成像处理，对测试数据进行处理的模板，可以显示二维像(ISAR imaging processing, the test data processing template, you can display the 2D image )

基于mfc对话框开发的图片位深度转换器，源码部分截图如下。 CImage img;    img.Load("D:\qrcode.png");    img.Save("D:\qrcode.bmp");    CxImage ximage;    ximage.Load("D:\qrcode.bmp", CXIMAGE_FORMAT_BMP);    ximage.AlphaStrip();    ximage.Save("D:\11.bmp", CXIMAGE_FORMAT_BMP);

一种简单高效地基于压缩感知的跟踪算法。首先利用符合压缩感知RIP条件的随机感知矩对多尺度图像特征进行降维，然后在降维后的特征上采用简单的朴素贝叶斯分类器进行分类。该跟踪算法非常简单，但是实验结果很鲁棒，速度大概能到达40帧/秒(A simple and efficient tracking algorithm based on compressed sensing. Firstly, with the random sensing matrix compressed sensing RIP conditions for multi-scale image feature dimension reduction, and then use the naive Bias classifier simple classification in the feature reduction after the. The tracking algorithm is very simple, but the results are robust, speed can reach 40 frames per second)

对于MATLAB双目视觉处理中，通过前期获取摄像机参数等后制成深度图。(For MATLAB binocular vision processing, we can get the depth map after obtaining camera parameters in the early stage.)

说明：  利用K均值聚类对图像lena进行分割，图片可根据需求进行更换。(Image Lena is segmented by k-means clustering, and the image can be replaced according to the demand.)