Introduction to Computer Vision 2018/2019

Mathieu Aubry, Karteek Alahari, Ivan Laptev, and Josef Sivic


Course Information

Class time: Thursday 9:00 - 12:00

Room: R

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.

Assignments

There will be four/five 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. Each project is based on a paper and a list of suggested papers is available here. Feel free to ask for papers on a topic that you are interested in or propose a paper (in this case, it has to be validated before the November 8th)

You are expected to understand and present the paper, but also to offer some added value, such as experiments of your own, new interesting tests with available code, or comparison with other relevant works. This will have to be adapted depending on the paper. You will have to present your project (10 minutes + 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. The assignments and final projects will be checked to contain original material. Any uncredited reuse of material (text, code, results) will be considered as plagiarism and will result in zero points for the assignment / final project. If a 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 13

MA

Introduction, overview, image formation, digital photography

PDF

PDF

Low level Computer Vision, image correspondences and grouping

2

Sept 20

MA

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 ,

PDF

PDF

3

Sept 27

MA

Edges (Canny), Segmentation (K-means, GMM, Mean shift), Points (Harris Corners, blob detection)

TP on Canny Edges (html , ipynb, lena.jpg, tools.jpg)

PDF

PDF

4

Oct 4

JS

Instance recognition, Feature detectors and descriptors, SIFT, Visual search

PDF

5

Oct 11

KA

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

Assignment 1 Due (Canny edges)

PDF

3D reconstruction

6

Oct 18

MA

Human color perception, color in computer vision

Human 3D  vision/perception

Projective geometry, camera matrix

refs : Forsyth and Ponce "Geometric camera model" chapter

Szeliski chapter 2 "Image formation" + printed introduction

TP2 on mean-shift segmentation (html , ipynb)

PDF

PDF

7

Oct 25

MA

camera calibration, Stereo vision

Nov 1

No lecture

Assignment 2 Due (Mean Shift clustering)

8

Nov 8

MA

multi-view reconstruction

Project choice due

Recognition

9

Nov 15

MA

Introduction to category-level recognition / Introduction to CNNs

10

Nov 22

MA

analyzing CNNs, CNNs for object detection and semantic segmentation

Assignment 3 Due (camera calibration)

11

Nov 29

MA

3D shape analysis (ICP, shape geometry, shape descriptors, CNN based)

Video

12

Dec 6

MA

Optical Flow: optical flow equation, Lukas-Kanade, Horn and Schunk, SIFT-flow, large displacement optical flow

Assignment 4 Due (MNIST recognition with NN)

13

Dec 13

KA

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

14

Dec 20

IL

High level video analysis, action recognition

Optional Assignment Due (Optical flow)

Dec 27

No lecture

Jan 3

No lecture

Overview of other/advanced topics

15

Jan 10

MA

TP feedback

Intro to Computer Graphics

Project reports due

16

Jan 17

MA

Final projects presentation

17

Jan 24

MA

Final projects presentation

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.

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/