precondition

压缩感知图像重构的算法，直接观测原始信号（二维），最后重建(Compressed sensing image reconstruction algorithm, direct observation of the original signal (two-dimensional), and finally reconstruction)

基于非下采样轮廓波变换的图像融合。低频分量取平均，高频分量取绝对值最大。(nonsubsampled contourlet transform.Averaged low frequency components, high frequency components of the maximum absolute value.)

基于c5509的jpeg图像压缩实现 ，利用c语言(The jpeg image compression based c5509 achieved using c language)

拐点的检测，对一个图像的拐点进行检测，使用的是freeman链码的方法，检测图像中拐点的方向，确定该方向是不是拐点。(Inflection point detection, an image of the inflection point for testing, use freeman chain code method, testing the image in the direction of the inflection point, we determine the direction is inflection point. )

This extracts features images, that can be used to detect humans and objects.

基于数字图像处理，实现一个车牌分割和识别的仿真系统。要求系统具有界面并实现以下功能： 1）读入交通标志图像； 2）预处理及车牌标志定位； 3）多种图像分割功能； 4）形态学滤波等功能； 5）对标志中的数字进行识别。(Based on digital image processing, segmentation and recognition of a license plate emulation system. Interface and require a system with the following functions: 1) read the traffic sign 2) pre-processing and positioning plate mark 3) a variety of image segmentation functions 4) morphological filtering and other functions 5) to mark the numbers Identification.)

批量读取tiff影像并计算月份NDVI最大值(use matlab to read tiff and calculate NDVI)

说明：  find_center:寻找图像的形心，即图像几何中心点的检测。 otsu:otsu分割 sobelNedge：sobel边缘检测 catch_point:全局阈值分割函数(Find center: find the centroid of the image, that is, the detection of the geometric center of the image. Otsu: Otsu segmentation Sobel edge: Sobel edge detection Catch point: global threshold segmentation function)

说明：  分子图像处理的软件，但没有数据库，具体操作看操作说明(Molecular image processing software, but there is no database, see the specific operation instructions)

说明：  SIFT特征点的提取和匹配，SIFT算法首先在尺度空间上进行关键点检测，确定关键点的位置和关键点所处的尺度，然后使用关键点邻域梯度的主方向作为该点的方向特征，最后由关键点邻域梯度信息生成关键点的特征向量(SIFT algorithm firstly detects the key points in the scale space, determines the location of the key points and the scale of the key points, then uses the main direction of the key point neighborhood gradient as the direction feature of the point, and finally generates the feature vector of the key points from the key point neighborhood gradient information)