计算机视觉有哪些比较好的公开课？补充计算机视觉基础。
19 个回答
稍微整理了一下收藏夹里的资料，分成了三类。
· 带视频的课程，主要由Computer Vision, Parallel Programming, Artificial Intelligence组成
· 带PPT的课程，链接里面的Slides和Material都挺丰富的，参考它们的Schedule，可以定一下自己的先修课，学习路线，技术兴趣之类。
· 对于一个知识点的讲解，比如Structure from Motion
Courses with Video：
1. CAP 5415 – Computer Vision (University of Central Florida)
CRCV | Center for Research in Computer Vision at the University of Central Florida
2. Introduction to Computer Vision
https://www.udacity.com/course/introduction-to-computer-vision--ud810
3. Machine Learning for Computer Vision
http://vision.in.tum.de/teaching/ss2015/mlpractice_ss2015
4. Heterogeneous Parallel Programming (CUDA)
Coursera - Free Online Courses From Top Universities
5. Intro to Parallel Programming (CUDA)
https://www.udacity.com/course/intro-to-parallel-programming--cs344
6. High Performance Computing
https://www.udacity.com/course/high-performance-computing--ud281
7. Coding the Matrix: Linear Algebra through Computer Science Applications
Coursera - Free Online Courses From Top Universities
8. Machine Learning
https://www.coursera.org/learn/machine-learning
9. CS 188 – Intro to AI
10. Intro to Artificial Intelligence
https://www.udacity.com/course/intro-to-artificial-intelligence--cs271
11. Artificial Intelligence for Robotics
https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373
Courses with PPT:
1. CSE/EE486 Computer Vision I
2. COS429 Computer Vision
3. 6.819/6.869 Advances in Computer Vision
6.869 Advances in Computer Vision, Fall 2015
4. CS543/ECE549 Computer Vision
5. CS143 Introduction to Computer Vision
CS 143 Introduction to Computer Vision
Lectures with PPT or other materials
1. SFMedu: A Structure from Motion System for Education
基础部分
先看cs131 计算机视觉的基础知识
CS131 Computer Vision: Foundations and Applications (主要讲传统的边缘检测，特征点描述，相机标定，全景图拼接等知识，作业里包含了很多知识点，全做下来学到的绝对不少)
这课没找到视频，所以我是直接看的ppt,再做作业
如果想看视频，推荐
UCF Computer Vision Video Lectures进阶部分
再看cs231 计算机视觉的进阶版
CS231n: Convolutional Neural Networks for Visual Recognition(主要讲卷积神经网络的具体结构，各组成部分的原理和优化，作业很难，自己手写网络)