跌倒检测，采用OPENCV4 DNN开发，在英特尔的GPU上加速，大概每帧40MS左右，速度极快，假如您电脑上有别的GPU，可能跑不了，因为英特尔的OPENCV限制DNN在别的GPU上运行。一般的电脑，只要有英特尔的graphic就能跑，速度比纯CPU快多了

2017-2018学年第二学期，一张照片六道题，考试时间18.6.11

CV全功能自动投票软件由CV投票公司China Vote（中国投票）研发和发布，CV投票公司从2004年开始创建，正值网络投票开始时兴的时刻，由曾经做过百度程序员、瑞星程序员的几名主要技术人员联盟组建。并一直致力与开发投票系统，采用真实独立IP，按活动网站的要求来正规投票。CV投票公司拥有全国各地几万会员投票，技术强，代理投票可以快速地使您的票数超过其它竞争对手。所有的投票操作都是在网站规则下进行，相当于您发动了很多人在帮您投票，所以有很高的安全性。CV验证码识别投票器可完全模拟手工操作快速投票，无需在电脑前看管，有验证码以及需注册账号的投票都可以，可多台电脑同时使用，安全便捷，自由控制票数。突破了同类软件的纯模拟手工,采用GET,POST方式提交数据到服务器,提高了投票速度的5倍以上,减少人工疲劳.经过长时间的实践测试，功能更强大完善。图型识别功能，能够快速破解验证码，识别各大知名网站的图型验证码。部分网站还能使用代理IP或伪造IP等技术突破IP限制，能在局域网内或网吧里或通过路由器等不可更换IP的电脑上使用。自助投票功能简单易用，内置了通用的WebKit浏览器，这一核心技术使它能够

opencv3计算机视觉 python实现，电子版图书，入门必读。

Learning Open-CV 源码，开源分享本来就不应该收积分，所以我没有设置分数

基于opencv写的人头统计程序，可以直接运行!

指针仪表角度检测，采用HOG+SVM检测仪表，然后用直线检测求出角度

面试公司包括阿里系的高德地图，优酷，斑马网络，饿了吗，今日头条，华为2012实验室，拼多多，陌陌，以及一些自动驾驶相关公司

Hands-On Computer Vision with Julia: Build complex applications with advanced Julia packages for image processing, neural networks, and Artificial Intelligence Explore the various packages in Julia that support image processing and build neural networks for video processing and object tracking. Key Features Build a full-fledged image processing application using JuliaImages Perform basic to advanced image and video stream processing with Julia's APIs Understand and optimize various features of OpenCV with easy examples Book Description 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 com

OpenCV三大开发宝典之一。Mastering Open CV 不多解释。