包括： 1. Computer Architecture A Quantitative Approach, 6th Edition 2. Computer Networking: A Top-Down Approach, 8th Edition 3. Computer Systems: A Programmer’s Perspective, 3rd Edition 4. Computer Vision: Algorithms and Applications 5. Introduction to Algorithms, 3rd Edition 6. Introduction to Algorithms, 4th Edition 7. Thomas’ Calculus: Early Transcendentals, 14th Edition 8. Thomas’ Calculus in SI Units, 14th Edition 9. Thomas’ Calculus, 14th Edition 10.Thomas’ Calculus, 11th Edition 绝对物超所值!

计算机视觉OpenCV基础实验合辑（实验1234+扩展） 专栏地址： https://blog.csdn.net/weixin_53403301/category_12113705.html 实验一 图像预处理 https://blog.csdn.net/weixin_53403301/article/details/127976297 实验二 基元检测 https://blog.csdn.net/weixin_53403301/article/details/127976661 实验三 目标识别（卡证、卡号识别） https://blog.csdn.net/weixin_53403301/article/details/127977068 实验四 尺寸测量 https://blog.csdn.net/weixin_53403301/article/details/127977211 实验扩展 图像处理 https://blog.csdn.net/weixin_53403301/article/details/127977388

计算机视觉OpenCV基础教学讲义（Python版，包括理论、图例、PPT、实验、代码、手册） 专栏地址： https://blog.csdn.net/weixin_53403301/category_12113705.html 实验一 图像预处理 https://blog.csdn.net/weixin_53403301/article/details/127976297 实验二 基元检测 https://blog.csdn.net/weixin_53403301/article/details/127976661 实验三 目标识别（卡证、卡号识别） https://blog.csdn.net/weixin_53403301/article/details/127977068 实验四 尺寸测量 https://blog.csdn.net/weixin_53403301/article/details/127977211 实验扩展 图像处理 https://blog.csdn.net/weixin_53403301/article/details/127977388

halcon图像识别，输入车牌图像，进行车牌检测，将检测出来的车牌号码保存到本地，并在halcon图像窗口中显示。

内容概要：人工智能CV入门Vgg16迁移学习猫狗分类实战代码及数据集；本内容为使用Pytorch对计算机视觉中的Vgg16迁移学习进行实战编码。本内容包含了实战教程使用的数据集及代码的jupyter notebook 能学到什么：通过此资源你可以学习到如何通过pytorch框架及python语言进行简单的计算机视觉中的Vgg16迁移学习猫狗分类算法实战，你可以对该算法有更加深入的理解，并且你也可以获得更强的实战能力。

Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it's easy to use and lets you write easy-to-compile and efficient machine code. This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You'll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you'll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease. What you will learn Analyze image metadata and identify critical data using JuliaImages Apply filters and improve image quality and color schemes Extract 2D features for image comparison using JuliaFeatures Cluster and classify images with KNN/SVM machine learning algorithms Recognize text in an image using the Tesseract library Use OpenCV to recognize specific objects or faces in images and videos Build neural network and classify images with MXNet Who This Book Is For Hands-On Computer Vision with Julia is for Julia developers who are interested in learning how to perform image processing and want to explore the field of computer vision. Basic knowledge of Julia will help you understand the concepts more effectively.

yolov5网络剪枝代码

本资源源项目为PlotNeuralNet，我在使用源代码过程中遇到了一些问题，并且出于自己的需求进行了一些改进，修改后的代码可以在Windows系统下成功运行，可以绘制非正方形的网络结构图，且在我看来绘制结果更加美观。 资源适用于对展示卷积神经网络具体结构有需求的研究人员，用户在下载本项目后按照README官方教程中的Getting Started部分进行使用，简单学习过语法后便可以通过test_simple.py代码绘制自己的卷积神经网络结构并在同路径下生成PDF文件，官方还提供了LeNet, UNet等经典卷积神经网络的代码，用户可直接进行使用。

1.了解计算机视觉基础知识和应用 2.学会OpenCV基本操作 3.学习图像处理方法 4.学习人脸检测，人脸识别应用

通用技术知识框架搭建：视觉通用调试技术的推广，让应用人员有一个全 面的知识架构，不是一个一个项目学习，是整个面的铺开，以后知识可举一反三。