本书叙述如何使用Python中的OpenCV库实现视频捕捉、图像处理、目标检测等功能。(This book will show you how to use OpenCV s Python bindings to capture video, manipulate images, and track objects with either a normal webcam or a specialized depth sensor, such as the Microsoft Kinect.)
说明： 对高光谱图像进行分类，处理。使用tensorflow框架(Classify and process hyperspectral images. Using tensorflow framework)
说明： 铁轨线检测，可以完成对直轨和弯轨的检测，效果较好(Rail line detection, can complete the detection of straight rail and curved rail, the effect is good)
主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)
说明： 通过已经获取的新冠肺炎数据 预测未来走势 并用网页的形式动态呈现(The novel coronavirus pneumonia data are used to predict the future trend and to dynamically display it in webpage form.)
在linux系统中通过卷积神经网络实现图像风格迁移(Image style migration through convolution neural network in linux)
说明： 用于Inkscape扩展gcode文件使用(Use for Inkscape extended gcode file)
说明： FMCW信号处理是通过python脚本实现的，可以总结为： FMCW IF信号预处理 范围-时间地图构造（RTM） 多普勒时间地图构造（DTM） 角度时间地图构造（ATM）(FMCW signal process was implemented by python script, which can be sum up as: FMCW IF signal pre-process Range-Time-Map construction (RTM) Doppler-Time-Map construction (DTM) Angle-Time-Map construction (ATM))
以python语言为平台，对机载激光雷达原始数据Las格式和txt格式进行读取，并根据信息熵确定维度特征计算的最佳邻域半径，最后计算得到最佳邻域半径和最佳维度特征值(Based on Python language, the Las format and TXT format of airborne lidar raw data are read, and the optimal neighborhood radius is determined according to the information entropy. Finally, the optimal neighborhood radius and the optimal dimension eigenvalue are calculated.)
说明： 以python语言为平台，对机载激光雷达原始数据Las格式和txt格式进行读取，并根据信息熵确定维度特征计算的最佳邻域半径，最后计算得到最佳邻域半径和最佳维度特征值(Based on Python language, the Las format and TXT format of airborne lidar raw data are read, and the optimal neighborhood radius is determined according to the information entropy. Finally, the optimal neighborhood radius and the optimal dimension eigenvalue are calculated.)
说明： 改进的canny算法，边缘处理更加精确，瘦边效果改进力度大，可以借鉴参考(The improved Canny algorithm, edge processing more accurate, thin edge effect improved greatly, can be used for reference)
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.)
使用matlab进行图像去雾，有助于帮助刚入门的图像小白学习matlab图像处理(Matlab image defogging, helpful to help beginners learning image white matlab image processing)
说明： 使用matlab进行图像去雾，有助于帮助刚入门的图像小白学习matlab图像处理(Matlab image defogging, helpful to help beginners learning image white matlab image processing)
说明： 采用生成对抗网络（GAN）实现图像去模糊，用于图像的清晰化处理(The generated countermeasure network (GAN) is used to remove the blur of the image, which is used to clear the image)
首先对车牌识别系统的现状和已有的技术进行深入的研究，然后开发出一个基于 Python 的车牌识别系统，文中先对车辆图像进行高斯去噪、灰度化和边缘检测等预处理方法，然后用颜色特征和形态特征相结合的方法来确定车牌位置，用彩色分割法来完成车牌分割，最后，运用 SVM 分类训练器完成字符识别并使用Python 软件环境进行车牌识别的仿真实验。(License plate recognition based on SVM)
说明： 首先对车牌识别系统的现状和已有的技术进行深入的研究，然后开发出一个基于 Python 的车牌识别系统，文中先对车辆图像进行高斯去噪、灰度化和边缘检测等预处理方法，然后用颜色特征和形态特征相结合的方法来确定车牌位置，用彩色分割法来完成车牌分割，最后，运用 SVM 分类训练器完成字符识别并使用Python 软件环境进行车牌识别的仿真实验。(License plate recognition based on SVM)
说明： 基于SegNet实现了车道线的识别。里面包含已经训练好的模型。(Lane line recognition based on SegNet contains the trained model.)
说明： 卷积神经网络用于两幅遥感图像或者红外和可见光图像的融合(For fusion of two remote sensing images or infrared and visible images)
可以在ABaqus中模拟出三维多晶体，加入取向模拟晶粒的受力情况。(The three-dimensional polycrystal can be simulated in ABaqus, and the force of the grain can be simulated with the orientation.)
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