|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/computer-vision-basics
课程评论：没有评论
课程名称：计算机视觉基础 课程概述：本课程旨在帮助学习者理解计算机视觉的概念及其使命——使计算机像人类一样“看”和解释世界。学习者将学习该领域的核心概念，并对人类视觉能力进行介绍。课程将使他们能够识别计算机视觉的一些关键应用领域，并理解数字成像过程。课程涵盖计算机视觉的基础要素，包括数字信号处理、神经科学和人工智能。主题包括颜色、光线和图像形成；早期、中期和高级视觉；以及计算机视觉所需的基本数学知识。学习者将能够应用数学技术完成计算机视觉任务。 本课程适合对计算机视觉概念感兴趣的任何人，尤其是有意复习计算机视觉数学概念的人。学习者应具备基本的编程技能和经验（了解for循环、if/else语句），尤其是在MATLAB环境中（Mathworks提供相关基础教程）。学习者还应熟悉以下内容：基本线性代数（矩阵向量运算和符号）、三维坐标系和变换、基础微积分（导数和积分）以及基本概率（随机变量）。 课程材料包括在线讲座、视频、演示、实践练习、项目作业、阅读材料和讨论。学习者将通过使用MATLAB及其支持工具箱的在线实验室获得编写计算机视觉程序的经验。 * MathWorks提供了免费许可证，供学习者在课程期间安装MATLAB。 课程大纲： 1. 计算机视觉概述：讨论计算机视觉是什么，相关领域，历史及其关键里程碑，以及一些应用。 2. 颜色、光与图像形成：讨论颜色、光源，针孔和数码相机，以及图像形成。 3. 低级、中级和高级视觉：探讨David Marr提出的计算机视觉三层范式，以及低级、中级和高级视觉的内容。 4. 计算机视觉的数学知识：讨论计算机视觉中使用的数学，包括线性代数、微积分、概率等。
Name:Computer Vision Overview
Description:In this module, we will discuss what computer vision is, the fields related to it, the history and key milestones of it, and some of its applications.
Name:Color, Light, & Image Formation
Description:In this module, we will discuss color, light sources, pinhole and digital cameras, and image formation.
Name:Low-, Mid- & High-Level Vision
Description:In this module, we will discuss the three-level paradigm of computer vision that was proposed by David Marr. We will also discuss low, mid, and high level vision.
Name:Mathematics for Computer Vision
Description:In this lecture, we will discuss the Mathematics used in Computer Vision, which includes linear algebra, calculus, probability, and much more.
By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. * A free license to install MATLAB for the duration of the course is available from MathWorks.
