200912260016

仿qq截图功能，如下图

使用PCL库进行点云投影，朱德海《点云库PCL学习教程》第二章示例代码(Example code for Lidar cloud data projection)

：近距离目标超高分辨率微波成像技术在安全检测、非破坏性控制、生物医学等领域中有着非常重要的应用 价值和广阔的应用前景。该文利用基于窄脉冲形式的超宽带时域雷达系统研究近距离目标的高分辨率ISAR成像， 给出了仿真和实测结果。利用转台旋转目标，基于超宽带脉冲源和超宽带收发天线以及高性能取样示波器，配以同 步触发脉冲和自行研发的数据采集软件，搭建了该时域雷达实验系统，提出了准确获取目标回波信息的实验条件， 和实现回波延时精确校正的误差补偿方法。并针对采样时间窗内的杂波干扰，讨论了时域后向投影算法(Back Projection，BP)与背景对消技术相结合的成像算法，实现了分辨率为 8 mm 的近距离目标的成像，准确地反映了 目标的位置、形状和大小等信息。( The high resolution imaging of near field targets are interesting for security, non-destructive control and biomedical application. In this paper, high resolution near field ISAR imaging is implemented using Ultra Wide Band (UWB) time domain radar system. Simulation and experiment are both conducted. The target is rotated by turntable. The system is built based on the UWB pulse source, UWB antennas, high performance sampling oscillograph, synchronous trigger pulse and proper motion study data acquisition software. Experiment condition and delay errors compensation method are presented to obtain accurate echo information of the target. And Back Projection (BP) combined with background cancel algorithm is discussed considering the clutter interference in time window. )

人脸检测：可自动检测人脸，速度还可以吧！(Face Detection : can automatically detect faces and can also speed!)

在对多光谱遥感影像进行大气校正处理以后，可以利用K_T变换矩阵对其进行水体提取，该算法效果较好(After the atmospheric correction of the multispectral remote sensing image, the water body can be extracted by using the k transform matrix, and the effect of the algorithm is good.)

说明：  python实现双目立体匹配SGBM算法(SGBM algorithm for binocular stereo matching based on Python)

说明：  Matlab编写的程序代码，包括基于肤色的人脸检测，根据人脸图像判别眼睛的状态（睁眼及闭眼）(Matlab program written in code, including color-based face detection, face image in accordance with the state of discriminant eyes (eyes open and eyes closed))

含实验报告以及测试数据 基于离散点的构TIN算法 一、算法思想 （1）首先，找出离散点集中相距最近的两点，连接这两点形成TI、的初始基线。  （2） 然后，找出包含此基线的另外一个点构成三角形。寻找第三点的方法: △按Delaunav三角形的判断法则乎找第三点。 △或者.按距离基线中点最近的原则挤找第三点。 （3）连接孰点与基线的两个端点形成两条新边，构成三角形。  （4）抓这两条新边为基线，重复上述过程。直到两条新边扩展完成。  （5）重复上述过程，依次循环处理所有新边。直到所有离散点均成为TIN的端点。

目前MATLAB里边对于多点计算仿射矩阵普遍采用RANSAC法，但是比较麻烦计算量也相对较大，通过最小二乘法来进行仿射矩阵的估计能够大大提高该效率，所以奉上自己在实验室写的仿射矩阵估计函数，来弥补matlab函数库在这方面的不足。(The MATLAB inside for calculating affine matrix commonly used RANSAC method, but more trouble calculation is relatively large, estimated by the least squares method to the affine matrix can greatly improve the efficiency, so give yourself in laboratory to write the estimated affine matrix function, to compensate for the lack of MATLAB Library in this area.)

关于自适应粒子滤波，我觉得很好用，贝叶斯滤波适合非线性和非高斯环境，在粒子滤波基础上 又加改进(About adaptive particle filter, I feel very good, a bayesian filter for nonlinear and non-gaussian environment, particle filter in again on the foundation and improvement)