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.
Lecture | Date | Instructor | Topic and reading materials. | Slides | Syllabus |
1 | Sept 13 | MA | Introduction, overview, image formation, digital photography | ||
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 , | ||
3 | Sept 27 | MA | Edges (Canny), Segmentation (K-means, GMM, Mean shift), Points (Harris Corners, blob detection) | ||
4 | Oct 4 | JS | Instance recognition, Feature detectors and descriptors, SIFT, Visual search | ||
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) | ||
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 | ||
7 | Oct 25 | MA | camera calibration, Stereo vision ref: Linear algebra for Vision (from Fei Fei Li): slides, pdf | ||
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 Assignment 3 Due (camera calibration) | ||
10 | Nov 22 | MA | Training CNNs Document on Stochastic Gradient Descent (Guillaume Obozinski) | ||
11 | Nov 29 | MA | analyzing CNNs, CNNs for object detection and semantic segmentation | ||
Video | |||||
12 | Dec 6 | MA | Optical Flow: optical flow equation, Lukas-Kanade, Horn and Schunk, SIFT-flow, large displacement and deep optical flow. Using synthetic data/3D shape analysis with CNNs | ||
13 | Dec 13 | KA | Low-level video analysis: tracking, human segmentation and pose Assignment 4 Due (MNIST recognition with NN) | ||
14 | Dec 20 | IL | High level video analysis, action recognition | ||
Dec 27 | No lecture | ||||
Jan 3 | No lecture | ||||
Overview of other/advanced topics | |||||
15 | Jan 10 | MA | TP feedback Intro to Computer Graphics/rendering Summary/perspectives Project reports due | ||
16 | Jan 17 | MA | Final projects presentation | ||
17 | Jan 24 | MA | Final projects presentation Optional Assignment Due (Optical flow) | ||
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/
