im2jpeg

两个l1准则下的噪声干扰信号压缩感知重构举例，两个例子的稀疏矩阵均为DCT矩阵，而观测矩阵分别采用单位阵和随机矩阵，有详细的步骤和使用方法，适用于初步的学习压缩感知方法。(This programme supply two examples by Compressed sensing with l1 norm. The sparse matrix of two examples are all DCT matrix and the obsever matrix are unit matrix and random matrix respectively. We give the detailed usage and steps for the programme which is useful to the beginner.)

图像检测，目标检测简单来说就是找出图像中的目标并给定目标的位置，一般用矩形框定位出目标(In image detection, target detection is simply to find out the target in the image and give the location of the target. In general, the rectangular box is used to locate the target)

说明：  MATLAB程序用于计算钢筋混凝土结构（梁，柱等）的非线性分析。(MATLAB program used to calculate the reinforced concrete structures (beams, columns, etc.) nonlinear analysis.)

SVM进行图像分割 使用OTSU阈值法 效果很好 分割出来直接是二值图像(SVM for image segmentation using the OTSU thresholding method works well split up directly from the binary image)

本程序和实现对一幅包含人脸的bmp图像进行光纤补偿，对人脸区域进行检测实现人眼和人嘴的检测与定位，最后勾绘出人脸的特征以实现人脸检测的目的。(This procedure and implementation of a compensate the fiber contains face of BMP image, the face region detection to realize human eyes and mouth detection and positioning, finally drew out the facial features to achieve the purpose of face detection. )

MATLAB编写的角点检测程序，用于对目标的检测和稳定的跟踪。很适合对于运动目标的精确跟踪。(Corner detection procedures written in MATLAB for target detection and tracking. Very suitable for the accurate tracking of moving targets.)

图像的傅里叶变换。在代码中实现了将两幅图像的幅度谱和相位谱进行交换，进而理解两者在图像显示中的作用(Fourier transform of the image. Implemented in the code will be two images amplitude spectrum and phase spectrum of exchange, and thus to understand both the role of the image display)

FDK算法 重建三维图像，期中有GPU并行计算加速fdk算法的实例(back-projection to RECONSTRUCT 3D IMAGE)

利用opencv库写的一个关于图像孔洞填补的小程序，附加了效果图片(Using the opencv library to write a small program on the filling of the hole, the additional effect of the picture)

运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。(Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training and test vehicle picture. Due to time reasons, the use of haar feature is not optimized, high dimension, resulting in sliding sash figure is too slow, the effect of the code only output statistics for your reference learning sparse representation in the vehicle detection.)