Introduction to Computer Vision 2020/2021

Mathieu Aubry, Gül Varol, Karteek Alahari, Ivan Laptev


Course Information

Class time: Tuesdays 9:00 - 12:00

Room:  E.Noether (ex UV)

News

Teaching Assistant: Robin Champenois, bonjour [at] robin-champenois [point] fr . Robin is your main contact for anything related to the programming assignments and final projects.

Pages from previous years: 2017-2018 ,2018-2019, 2019-2020

Evaluation
Assignments

There will be four programming assignments representing 60% of the grade. The supporting materials for the programming assignments projects will be in Python. You have to send your assignments to Robin by the deadline.

Final project

The final project will represent 40% of the grade. There can be two types of projects:

Projects are in group of 2 or 3. You will have to present your project (~10 minutes x number of persons in the group + questions) and return a summary (2 pages max) of the essential points that should be readable (and useful) for the other students in the class.

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 or derivations) and submit their own report. If you borrowed some code from somebody else, or wrote some code with somebody else, write it clearly in the report. Any uncredited reuse of material (text, code, results) will be considered as plagiarism and will result in zero points for the assignment. You all answer questions in very different ways, plagiarism is obvious when correcting assignments. If plagiarism is detected, the student will be reported to ENS.

Course schedule (subject to change):

Lecture

Date

Instructor

Topic and reading materials.

Slides

Syllabus

1

Sept. 22th

MA

Introduction, overview, Digital photography, projections and camera matrix

refs : Forsyth and Ponce "Geometric camera model" chapter

Szeliski chapter 2 "Image formation"

Linear algebra for Vision (from Fei Fei Li): slides, pdf

2

Sept. 29st

MA

color, Human 3D  vision/perception

Edges (Canny)

Linear and non-Linear Image filtering: Fourier and convolution, Bilateral Filter, Non-Local-Mean

Ressources: relevant Book chapters on Fourier and linear image filtering (chapt. 2 and 3); Detailed presentation of Bilateral Filter ,

3

Oct. 6th

MA

Classical feature detectors (Harris Corners, blob detection) and descriptors, local features

Instance recognition, RANSAC/Hough transform

Projective geometry, Stereo vision

printed introduction

4

Oct. 13th

MA

Camera calibration, multi-view reconstruction.

Assignment 1 Due (Canny edges)

5

Oct. 20nd

MA

Segmentation (K-means, GMM, Mean shift)

Optical Flow: optical flow equation, Lukas-Kanade, Horn and Schunk.

6

Nov. 3th

KA

Markov Random Fields: optimization methods (graph-cuts, belief propagation, TRW-S), applications to stereo and segmentation

Assignment 2 Due (camera calibration)

7

Nov. 10th

KA

Markov Random Fields: optimization methods (graph-cuts, belief propagation, TRW-S), applications to stereo and segmentation

8

Nov. 17th

MA

Intro to Computer Graphics (intro to classical shape analysis)

Introduction to category-level recognition / Introduction to CNNs

Project choice finalized

Assignment 3 Due (Mean Shift clustering)

9

Nov 24th

MA

Training CNNs

Document on Stochastic Gradient Descent (Guillaume Obozinski)

10

Dec. 1rd

GV

visualizing/understanding CNN, Image classification, Transferring representations

11

Dec. 8th

GV

CNNs for object detection and semantic segmentation, more applications: 3D shape analysis/generation with NNs

Assignment 4 Due (MNIST recognition with NN)

12

Dec 15th

IL

Low-level video analysis: tracking, human segmentation and pose

13

Jan. 5th

IL

High level video analysis, action recognition

Project summary due

14

Jan. 12th

MA

Projects presentations

15

Jan. 19th

MA

Projects presentations

Relevant literature:

[1]

D.A. Forsyth and J. Ponce, "Computer Vision: A Modern Approach", Prentice-Hall, 2nd edition, 2011

[2]

J. Ponce, M. Hebert, C. Schmid and A. Zisserman "Toward Category-Level Object Recognition", Lecture Notes in Computer Science 4170, Springer-Verlag, 2007

[3]

O. Faugeras, Q.T. Luong, and T. Papadopoulo, "Geometry of Multiple Images", MIT Press, 2001.

[4]

R. Hartley and A. Zisserman, "Multiple View Geometry in Computer Vision", Cambridge University Press, 2004.

[5]

J. Koenderink, "Solid Shape", MIT Press, 1990

[6]

R. Szeliski, "Computer Vision: Algorithms and Applications", 2010. Online book.

[7]

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. Online book

Good and relevant lectures by other people (many slides are taken from them)

James Hays https://www.cc.gatech.edu/~hays/compvision/

Svetlana Lazebnik http://slazebni.cs.illinois.edu/spring18/ 

Derek Hoeim https://courses.engr.illinois.edu/cs543/sp2017/ 

David Fouhey https://web.eecs.umich.edu/~fouhey/teaching/EECS442_W19/schedule.html