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Intro to Computer Vision

NYU, Fall 2019

Special Topics in Data Science, DS-GA 3001.005/.006



Jean Ponce (
Matthew Trager (

Jiachen Zhu (
Sahar Siddiqui (


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

Collaboration policy: You can discuss the assignments and final projects with other students in the class. Discussions are encouraged and are an essential component of the academic environment. However, each student has to work out their assignment alone (including any coding, experiments, and derivations) and submit their own report/notebook.




Week Date Topic Instructor Slides
1 9/3 Course overview, image formation JP Slides
2 9/10 Camera geometry and calibration I JP Slides
3 9/17 Camera geometry and calibration II JP Slides
4 9/24 Linear and nonlinear filters, edge detection MT Slides
5 10/1 Interest points (Harris, SIFT), robust estimation (RANSAC, Hough transform) MT Slides
6 10/8 Radiometry and color JP Slides
  10/15 No class    
7 10/22 Texture and image segmentation MT Slides
8 10/29 MRF and graph cuts, single-view geometry MT Slides
9 11/05 Epipolar geometry JP Slides
10 11/12 Stereo JP Slides
11 11/19 Structure from motion JP Slides
12 11/26 Intro to recognition, ML, and deep learning MT Slides
13 12/03 Neural networks, object classification and detection MT Slides
14 12/10 Weakly supervised and unsupervised methods JP Slides


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.