LS_Unwrape

将经过二值化的车道线图利用hough变换提取车道线(After binarization the lane line graph using hough transform to extract lane)

用matlab编写多级非标准小波重构的函数 (prepared using Matlab multi-level non-standard wavelet function Reconstruction)

基于opencv的radon直线检测、直方图均衡、sobel边缘检测等算法。(Opencv of the radon-based line detection, histogram equalization, sobel edge detection algorithm.)

SAR面目标回波仿真 包含3个目标三维矩阵的mat文件 分别对着三种不同面目标可以进行回波仿真 直接运行即可(The SAR surface targets echo simulation contains 3 target three-dimensional matrix mat file against the three different surface targets echo simulation can be run directly)

下面是利用卡尔曼滤波的方法，实现雷达对目标的跟踪:一目标沿水平方向运动，起始位置为（－2000米，1000米），运动速度为15米/秒，扫描周期T＝2秒， 米，采用蒙特卡洛方法对跟踪滤波器进行仿真，仿真次数为100次。(Below is the use of Kalman filtering method, to achieve the target tracking radar. Parameter :: a target in a horizontal direction, the starting position (2000m, 1000m), the motion speed of 15 m/s, the scan period T = 2 seconds, rice, Monte Carlo method using the tracking filter simulation, simulation 100 times.)

用PHD滤波的方法实现多目标跟踪，及对PHD滤波性能的检测。(PHD filter method using multi-target tracking, and performance testing for the PHD filter.)

粘连字符切分，带测试图片，效果不错 (Adhesion character segmentation, with test images, good results)

说明：  本程序在EVC4.0+PPC SDK 2003下调试通过。用Independent JPEG Group发行的JpegLib进行Jpeg图像的读取与保存。(This procedure in EVC4.0+ PPC SDK 2003 adopted under the debugger. By Independent JPEG Group issued JpegLib to read Jpeg images and preservation.)

该算法首先通过字典学习得到含噪图像的冗余字典，然后对相似的图像块进行聚类构成块群，并通过迭代收缩和L1正则化约束，对同类的图像块在字典上进行稀疏表示，以达到降噪的目的。实验结果表明，在常规的图像处理上，本文提出的算法能较好的保留图像的结构信息，与K-SVD和BM3D等现有的流行算法相比，具有更高的峰值信噪比（PSNR）(It firstly get the redundant dictionary of a noised image by dictionary learning.Then,the image patches are gathered according to their similarities.Meanwhile,the similar patches get sparse representation showed in dictionaries by iterative shrinkage and L1 regularization constraints and eventually the image is restored and noise is removed.The experimental results indicate that the proposed algorithm can well preserve the structure information of the common image with a higher Peak Signal to Noise Ratio(PNSR),compared with state-of-the-art algorithms,such as K-SVD and BM3D)

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)