CS231A: Computer Vision, From 3D Reconstruction to Recognition

Course Description

An introduction to the concepts and applications in computer vision. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization. Prerequisites: linear algebra, basic probability and statistics..

Instructor

Silvio Savarese

Teaching Assistants

Amir Sadeghian (Head CA)

Kuan Fang

Danfei Xu

Fei Xia

Julian Gao

Class Time and Location

Winter quarter (Jan- March, 2018).
Lecture: Monday, Wednesday 1:30pm-2:50pm
Skillaud (map)
Section: Friday 11:30am-12:20pm, Gates B01

Office Hours

Kuan Fang: Mon 4:30pm-6:30pm, Gates 259
Danfei Xu: Tue 9:00am-11:00am, Gates 167(before Feb), 159(start from Feb)
Fei Xia: Wed 4:30pm-6:30pm (starts 2nd week), Gates 167
Julian Gao: Thu 2:00pm-4:00pm, Gates B21
Amir Sadeghian: Fri 3:00pm-5:00pm, Gates 159
Silvio Savarese : Fri 2:00pm-3:00pm (starts 2nd week) , Gates 154
[Calendar] [SCPD OH Hangout Link, click to join call]

Grading Policy

See the Grading Page for more details on grading.

Course Discussions

Stanford students: Piazza

Assignment Details

See the Assignment Page for more details on how to hand in your assignments.

Course Project Details

See the Project Page for more details on the course project.

Prerequisites

Proficiency in Python, high-level familiarity in C/C++
All class assignments will be in Python (and use numpy) (CS231N provides a very nice tutorial here for those who aren't as familiar with Python), but some of the deep learning libraries that you may want to use for your projects are written in C++. If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript) you will probably be fine.
College Calculus, Linear Algebra (e.g. MATH 19 or 41, MATH 51)
You should be comfortable taking derivatives and understanding matrix vector operations and notation.
Basic Probability and Statistics (e.g. CS 109 or other stats course)
You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.
Equivalent knowledge of CS131, CS221, or CS229.
You should be familiar with basic machine learning or computer vision techniques.

FAQ

Can I take this course on credit/no credit basis?

Yes. Credit will be given to those who would have otherwise earned a C- or above.

Can I audit or sit in?

In general we are very open to sitting-in guests if you are a member of the Stanford community (registered student, staff, and/or faculty). Out of courtesy, we would appreciate that you first email us or talk to the instructor after the first class you attend. If the class is too full and we're running out of space, we would ask that you please allow registered students to attend.

Is there a textbook for this course?

This year we are trying to make our own self-contained course notes. However, we do recommend some some textbooks for this course, and they usually can be found at Stanford Libraries. The recommended textbooks are

D. A. Forsyth and J. Ponce. Computer Vision: A Modern Approach (2nd Edition). Prentice Hall, 2011.
R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2003. link

Can I work in groups for the Final Project?

Yes, in groups of up to three people.

I have a question about the class. What is the best way to reach the course staff?

Stanford students please use an internal class forum on Piazza so that other students may benefit from your questions and our answers. If you have a personal matter, email us at the class mailing list cs231a-win1718-staff@lists.stanford.edu.

Can I combine the Final Project with another course?

Yes, you may. There are a couple of courses concurrently offered with CS231A that are natural choices, such as CS231N (Convolutional Neural Networks, by Prof. Fei-Fei Li). Speak to the instructors if you want to combine your final project with another course.