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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.)

说明：  可见光影像的密集匹配，很经典的算法，效果也非常理想(image matchingimage matching)

tld目标跟踪算法，很适合初学者，实时性很好。(To promote the informationization of the global Internet revolution, for global range of information processing and communication provides the operating platform,)

人脸识别代码,K_L变换的人连识别方法,(人脸识别代码)

运用计算机图形学的知识，采用4阶B样条基函数，节点向量取为[0,0,0,0,1/4,2/4,3/4,1,1,1,1] 先绘制控制多边形，再用分段直线段绘制B样条曲线(Using the knowledge of computer graphics, the 4 order B spline basis function is used, and the node vector is taken as [0,0,0,0,1/4,2/4,3/4,1,1,1,1]. First, draw the control polygon and draw the B spline curve by piecewise straight line segments.)

输入待分解图像的数据矩阵 可以显示原始图像以及其尺度2的小波分解图 小波包函数可在函数中自行修改(The input matrix of the image to be decomposed can display the original image and the wavelet packet function of the wavelet decomposition graph of scale 2 can be modified by itself in the function.)

简单的graph cut 算法，初学者适合(Simple graph cut algorithm for beginners)

Guilde Filter 图像滤波； 包含[2015]Fast Guilded filter.pdf ; [2013] Guided Image Filtering.pdf 经典。(inculde classical paper for guilded filter)

说明：  人脸识别的基于独立份量分析文章，英文原版，pdf格式。(an article about face recogoniton based on independent component analysis . original english edition,pdf format. )

计算机视觉文本介绍，包括研究背景、意义、以及应用(Text of computer vision, including research background, significance, and application)