Schedule and Syllabus

Lectures are held on Tuesdays and Thursdays from 1:30pm to 3:00pm @ Hewlett 201, also Zoom.

Recitations are held on select Fridays from 12:15pm to 1:15pm @ Building 200-230, also Zoom .

Students with Documented Disabilities: Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty. Unless the student has a temporary disability, Accommodation letters are issued for the entire academic year. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. The OAE is located at 563 Salvatierra Walk (phone: 723-1066, URL:

This is the syllabus for the Fall 2021 iteration of the course.

Homework releases can be found on GitHub.

Event Type Date Description Pre-recorded Materials Live Lecture Materials
Lecture 1 Tuesday
September 21
Course introduction and Logistics
[1.1 What is Computer Vision]
[1.2 Computer Vision Applications]
I. Low-Level Vision (this part will be flipped classroom)
Lecture 2 Thursday
September 23
Photometric Image Formation
[2.1 Images, Sampling and Quantization]
[2.2 Color Physics]
[2.3 Color Matching]
[2.4 Color Constancy]
Recitation 1 Friday
September 24
Python/NumPy Review I
Lecture 3 Tuesday
September 28
[3.1 Linear Systems]
[3.2 Convolution and Correlation]
Lecture 4 Thursday
September 30
[4.1 Edge Detection Overview]
[4.2 Image gradients]
[4.3 A simple edge detector]
[4.4 Sobel edge detector]
[4.5 Canny edge detector]
[4.6 Hough Transform for Line Detection]
Recitation 2 Friday
October 1
Linear Algebra Review
HW0 Due Friday
October 1, 11:59pm
Homework #0 due
[Homework #0]
Lecture 5 Tuesday
October 5
Features and Matching
[5.1 Ransac]
[5.2 Local invariant features]
[5.3 Harris Corner Detector]
Lecture 6 Thursday
October 7
Features and Matching
[6.1 Scale invariant keypoint detection]
[6.2 SIFT]
[6.3 HoG]
Recitation 3 Friday
October 8
HW1 Due Friday
October 8, 11:59pm
Homework #1 due
[Homework #1]
Lecture 7 Tuesday
October 12
Optical Flow
[7.1 Optical Flow]
[7.2 Lukas-Kanade method]
[7.3 Pyramids for Large Motion]
[7.4 Horn-Schunk method]
[7.5 Applications]
II. Geometric Vision (this part will be live)
Lecture 8 Thursday
October 14
Geometric Image Formation
[Why Geometric Vision Matters]
[Geometric Primitives in 2D & 3D]
[2D & 3D Transformations]
Recitation 4 Friday
October 15
Python/NumPy Review II
HW2 Due Friday
October 15, 11:59pm
Homework #2 due
Edge Detection
[Homework #2]
Lecture 9 Tuesday
October 19
Pinhole Camera Model
[From 3D to 2D: the Pinhole Camera Model]
[Properties of Perspective]
[Monocular cues for 3D]
Lecture 10 Thursday
October 21
Camera Calibration
[Camera Calibration: Estimating intrinsics K]
[Pose Estimation: Estimating extrinsics [R|t]]
HW3 Due Friday
October 22, 11:59pm
Homework #3 due
[Homework #3]
Lecture 11 Tuesday
October 26
Multi-view Geometry
[Epipolar geometry]
Lecture 12 Thursday
October 28
Structure from Motion
[SfM Problem Definition]
[Finding 2D Correspondences]
[Solving for Cameras and 3D Points]
HW4 Due Friday
October 29, 11:59pm
Homework #4 due
Panorama Stitching
[Homework #4]
III. Visual Pattern Recognition (this part will be live)
November 2
No class (Stanford Democracy Day)
Lecture 13 Thursday
November 4
[13.1 Introduction]
[13.2 Gestalt theory]
[13.3 Agglomerative clustering]
[13.4 Oversegmentation]
HW5 Due Friday
November 5, 11:59pm
Homework #5 due
Camera + Calibration
[Homework #5]
Lecture 14 Tuesday
November 9
[14.1 K-means]
[14.2 Mean-shift]
Lecture 15 Thursday
November 11
Supervised & Deep Learning
[15.1 ML For CV: A Brief Overview]
IV. Advanced Topics (this part will be live)
Lecture 16 Tuesday
November 16
Understanding Videos
[16 Understanding Videos]
Lecture 17 Thursday
November 18
Vision for Robotics & Self-driving
[17 Self-Supervised 3D Vision]
Lecture 18
Guest Lecture by Vincent Sitzmann
November 30
Self-supervised Scene Representation Learning
Lecture 19
Guest Lecture by Anna Bethke
December 2
The FATE of AI Ethics: How to research AI in a fair, accountable, transparent, explainable, and ethical manner