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SAR影像cloude分解，提取极化信息(PolSAR cloude decomposition)

极易分词源码包,好用最新版本,双向匹配分词(vulnerable segmentation source package, the latest version easier to use, two-way matching word)

图像移动，随着鼠标移动图像，可以自由在空间内平移(image auto move)

本程序是一幅512*512的lena.raw图像进行快速DCT和其逆变换，整个程序均调试通过，是在C语言环境下编写的，在整个关于快速DCT的源码中，本代码可以说是集大成者，很值得一看。(This procedure is mainly of a float or double type of data, rounding, the float data can be positive or negative, can be, take the whole principle of rounding.)

在道路交通管理中，为了获得车辆的运动数据，早期经常采用的是感应线圈等硬件测量的方法。而如果采用摄像头拍摄的道路视频，再用计算机软件处理的方法，则可以极大的增加方便性和灵活性。本文运动目标检测与跟踪研究如何让计算机从视频图像序列中获得物体运动数据。(Traffic management in order to obtain motion data of the vehicle, is a method often used early induction coil measurement hardware. If using a video camera to shoot on the road, and then deal with methods of computer software, you can greatly increase the convenience and flexibility. This paper studies the moving target detection and tracking moving objects how to get data from a computer video image sequence.)

SART图像重建代码 基于C++ 主要用于CT等射线处理(code of image and with a wide application of CT, x-RAY , ETC)

他很反感；归属地分块地方好法国号法国号韩国胡富国(He was disgusted attribution to block a good place to Hu Fuguo French horn French horn Korea)

UTIL MOTION DETECTION AND TRACKING

说明：  UNet最早发表在2015的MICCAI上，短短3年，引用量目前已经达到了4070，足以见得其影响力。而后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。而如今在自然影像理解方面，也有越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FPN)(CVPR 2017)等。(Its influence has reached 70% in 2015. Then it became the baseline that most of the medical image semantic segmentation tasks, and inspired a large number of researchers to think about the U-shaped semantic segmentation network. In the aspect of natural image understanding, more and more SOTA models of semantic segmentation and object detection begin to pay attention to and use U-shaped structure, such as semantic segmentation, discriminative feature network (DFN) (cvpr2018), feature pyramid networks for object detection (FPN) (CVPR 2017), etc.)

可以采用形态学方法找出边界，比较有益的初学者的学习方法。(It s very good for a newer to learning about image processing.)