View on GitHub

Intro to Computer Vision

NYU, Spring 2021

Source: Willow

Course webpage for the NYU Spring 2021 Course: Introduction to Computer Vision. This course is aims to cover a broad topics in computer vision, and is not primarily a deep learning course. We will covert topics in traditional computer vision such as camera geometry, image formation, segmentation, object recognition, classification, and detection (see Syllabus).

Special Topics in Data Science, DS-GA 3001.004/.005



Jean Ponce (
Elena Sizikova (


Aryaa Singh (
Manjusha Mishra (


Four programming assignments (60% of the grade) + final project (40% of the grade). Assignments should be submitted using the NYU class site.



We do not require purchase of any textbooks and the course will be self-contained. You may wish to consult the resources below for additional material formalization.


Note: lecture slides will be posted after each lecture.

Date Lecture Topic Link
01/28 Lab: Course Intro Introduction, Logistics Intro
[Key] [PDF]
02/03 Lecture 1 Examples of vision tasks, camera geometry and calibration Lecture [PPT] [PDF]
02/04 Lab A detour of sensing country, intrinsic and extrinsic parameters Lecture [PPT] [PDF]
02/10 Lecture 2 Linear calibration, analytic photogrammetry, filtering Lecture Part 1
Lecture Part 2
[Key] [PDF]
02/11 Lab Image Stitching Exercise Notebook Link
02/17 Lecture 3 Edge detection, keypoints and features, RANSAC, Hough transform Lecture [Key] [PDF]
02/21 Exercise 1 DUE    
02/24 Lecture 4 Texture, Segmentation Lecture [Key] [PDF]
02/25 Lab Edge Detection using Sobel Operator Notebook Link
03/03 Lecture 5 Radiometry and Color, Part 1 Lecture [PPT] [PDF]
03/04 Lab Radiometry and Color, Part 2 Lecture [PPT] [PDF]
03/10 Lecture 6 Color Lecture [PPT] [PDF]
03/11 Lab Canny Edge Detection Skeleton Code Notebook Link
03/14 Exercise 2 DUE    
03/17 Lecture 7 Stereopsis, Epipolar Geometry, Essential and Fundamental Matrices Lecture [PPT] [PDF]
03/18 Lab Fundamental Matrix Esimation Notebook Link
03/24 Lecture 8 Eight-point Algorithm, Correlation-based stereo, more sophisticated methods Lecture [Key] [PDF]
03/25 Lab Eight-point Algorithm Notebook Link
03/31 Lecture 9 Structure from Motion Lecture [PPT] [PDF]
04/01 Lab    
04/04 Exercise 3 DUE    
04/07 Lecture 10 Affine and Projective SfM Lecture [PPT] [PDF]
04/08 Lab    
04/14 Lecture 11 SfM, Laser Scanning, Space Carving, Multiview Stereo P1[PPT] P1[PDF]
P2[Key] P2[PDF]
04/15 Lab ICP Implementation Notebook Link
04/21 Lecture 12 Visual Recognition, Intro to NN P1[PPT] P1[PDF]
P2[Key] P2[PDF]
04/22 Lab VAE Implementation Notebook Link
04/28 Lecture 13 CNN Recognition and Detection Lecture [Key] [PDF]
04/29 Lab    
05/02 Exercise 4 DUE    
05/05 Lecture 14    
05/06 Lab    


Much of the material for this course relies on the Computer Vision course given at ENS Paris by Mathieu Aubry, Karteek Alahari, Ivan Laptev, and Josef Sivic. Many of the slides are taken from James Hays, Svetlana Lazebnik, and Derek Hoeim. Website was originally designed by Matthew Trager.