工业视觉基础知识，文档资料

目录内容说明基础概念视觉伺服基于图像IBVS几何解释双目系统基于位置PBVS 待研究 内容说明 本文主要为 HAL Id: inria-00350283的学习记录 感谢原作者 该系列文章使用伺服回路中的计算机视觉数据来控制机器人的运动。 首先给出视觉伺服控制问题的一般性概述。然后介绍了两种典型的视觉伺服控制方案：基于图像的视觉伺服控制和基于位置的视觉伺服控制。 最后，我们讨论了与这两个方案相关的性能和稳定性问题。 基础概念 刚体运动与相机模型了解 视觉伺服 视觉伺服控制是一种基于视觉反馈的控制方式。是我们在日常生活中每时每刻都在进行的一种反馈：当你伸出手去抓取桌上的杯子的时候，眼睛会不断的反

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

基于python的单目与双目视觉三维重建设计与实现

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。

吉林大学海量视觉检索技术课程实验报告任务二 1、通读论文内容。 2、依据论文内容完成如下函数： 2.1 energyImage = energy_image(im)，依据每个像素在X和Y方向的梯度幅值完成能量图的计算，其中输入图像im为MxNx3 格式为unint_8;输出energyImage为double型的矩阵； 2.2 cumulativeEnergyMap = cumulative_minimum_energy_map (energyImage, seamDirection)；依据论文内容计算累计最小能量映射图，其中输入的energyImage为2.1的输出，seamDirection可选为“HORIZONTAL”或“VERTICAL”。输出为2D double型矩阵。 2.3 verticalSeam = find_optimal_vertical_seam(cumulativeEnergyMap) ；依据2.2中的函数计算最优垂直缝，其输入为2.2的输出，输出必须是包含像素的列索引的向量，以此形成每行的接缝。 2.4 horizontalSeam = find_op

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.