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:

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.

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.

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 short 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

1

Sept 13

MA

Introduction, overview, image formation, digital photography

Low level Computer Vision, image correspondences and grouping

2

Sept 20

MA

Image filtering: convolution, derivation, Canny,Bilateral Filter, Bilateral Filter and applications, Non-Local-Mean

3

Sept 27

MA

Human color perception, color in computer vision, grouping in human perception

Segmentation: K-means, GMM, Mean shift

4

Oct 4

JS

Feature detectors and descriptors, SIFT

Assignment 1 Due (Canny edges)

5

Oct 11

KA

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

3D reconstruction

6

Oct 18

MA

Human 3D  vision/perception

Projective geometry, camera matrix

refs : Forsyth and Ponce "Geometric camera model" chapter

Szeliski chapter 2 "Image formation"

7

Oct 25

MA

Stereo vision

Assignment 2 Due (Mean Shift clustering)

Nov 1

No lecture

8

Nov 8

MA

camera calibration, 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/