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.
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" | ||
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