Discussion group:Piazza.
Please post all your questions on the discussion board so that others may learn from your questions as well. Do
not email the professor or TA directly with homework questions.
Course description: The goal of computer vision is to compute properties of the three-dimensional
world
from digital images. Problems in this field include reconstructing the 3D shape of an environment,
determining
how things are moving, and recognizing people and objects and their activities, all through analysis of
images
and videos.
This course will provide an introduction to computer vision, including such topics as image formation,
feature
detection, motion estimation, image mosaics, 3D shape reconstruction, and object recognition. Applications
of
these techniques include building 3D maps, creating virtual characters, organizing photo and video
databases,
human-computer interaction, video surveillance, and automatic vehicle navigation. This is a project-based
course, in which you will implement several computer vision algorithms and do a final project on a research
topic of your choice.
Prerequisites
This course will assume a reasonable knowledge of linear algebra and calculus as a prerequisite. The programming
assignments will be in MATLAB, so a familiarity with MATLAB is essential. We will also upload some tutorial
videos of our own on Canvas to help you get started with MATLAB if you are new.
Please speak to the instructor if you are unsure of whether you can take the course.
Introduction to Neural Networks and Convolutional Neural Networks
T, Mar 14
no class (spring recess)
R, Mar 16
no class (spring recess)
T, Mar 21
Radiometry and Surface Appearance
R, Mar 23
3D Shape from Photometric Stereo
HW4 due, HW5 out
T, Mar 28
no class
R, Mar 30
no class
T, Apr 4
Shape from Focus/Defocus
Project mid-term report due
R, Apr 6
Camera Calibration and Shape from Stereo
HW5 due, HW6 out
T, Apr 11
Shape from Uncalibrated Stereo
R, Apr 13
Modern 3D Cameras: Structured Light and Time-of-Flight
T, Apr 18
Optical Flow and Motion Measurement
R, Apr 20
Image Tracking
HW6 due, HW7 out
F, Apr 21
Advanced Topics: Computational Cameras
T, Apr 25
Project presentations
R, Apr 27
Project presentations
T, May 2
Project presentations
R, May 4
Project presentations
HW7 due
F, May 5
Project webpage due
Coursework and grading
Grading will be based on a 100 point system. There are two main components: (a) homeworks (60% grade), and (b)
final research project (40% grade). Details about these components are given below.
In addition to the homeworks, we will also put up some quizzes on Canvas. However, they are optional, and will
not contribute to your grade. They are only to help you check your understanding of the course material.
Home-work assignments
The course will consist of 7 homework assignments. They will together account for 60% of your final grade. Some
homeworks are lighter than others; their relative weights in your final homework grade are determined by the
total number of points listed at the top of each homework. Guidelines for completing homework assignments are
given here. Please read these
carefully.
A discussion for each homework assignment will be created on Piazza. Please post all of your questions on the
discussion board so that others may learn from your questions as well. Do not email the professor or TA directly
with homework questions.
Include all the files in a zip file named hwX_yourNetID.zip (where X is the homework number) and upload the zip
file to Canvas. All homeworks are to be submitted by midnight on the due date.
Late days
Only for the homeworks (not project), students will be allowed a total of 5
(five) late days over the semester; each additional late day will incur a 20% penalty.
Final Research Project
The final project is research-oriented. It can be a pure vision project or an application of vision technique in
the student's own research area. You are expected to implement one (or more) related research papers, or think
of some interesting novel ideas and implement them using the techniques discussed in class. Students are
encouraged to propose their own project topics, subject to the instructor's approval. Some example projects from
previous years' offerings of the course are at this page.
You should work on the project in groups of 2-3. In your submission, please clearly identify the contribution of
all group members. Please note that members in the same group will not necessarily get the same grade.
NOTE: You can use any programming language you like for your projects.
Project timeline and what to submit
There will be three checkpoints: a project proposal, an intermediate milestone report, and a final project
webpage. Create a webpage for the project at the beginning. This page will include
the proposal, the mid-term report, interesting implementation details, some results (e.g., images, videos,
etc.), and so on. Your website should also include downloadable source and executable code. Do not modify the
website after the due date of the assignment. Upload the URL of your webpage in the corresponding
Canvas assignment BEFORE the due date.
Proposal (5%, due February 23)
This will be a short report (usually one or two pages will be enough). You will explain what problem you
are
trying to solve, why you want to solve it, and what are the possible steps to the solution. Please
include a
time table.
Mid-term report (5%, due April 4)
In this report, you will need to give a brief summary of current progress, including your current
results,
the difficulties that arise during the implementation, and how your proposal may have changed in light
of
current progress.
Final presentation (15%, from April 25 through May 4)
This will be a short presentation in class about your project. It will be 5 minutes per team. Please add
a
link to the presentation on the project webpage.
Webpage (15%, due May 5)
Assemble all the materials you have finished for your project in a webpage or wiki, including the
motivation, the approach, your implementation, the results, and discussion of problems encountered. If
you
have comparison results or some interesting findings, put them on the webpage and discuss these as well.
Readings
The material covered in the course will be available in the presentation slides which will be fairly
self-contained. The slides will be made available via Canvas.
The materials from this class rely significantly on slides prepared by other instructors, especially Shree
Nayar, Oliver Cossairt, Peter Belhumeur and Alyosha Efros. Also see other similar courses by Fredo Durand and
Bill Freeman (MIT), Peter Belhumeur (Columbia), Irfan Essa (Georgia Tech.), Steve Seitz (U. Washington) and
Kyros Kutulakos (U. Toronto). The instructor is extremely thankful to the researchers for making their notes
available online.
Academic Integrity
This course follows the University of Wisconsin-Madison Code of Academic Integrity. Unless specifically
authorized by the instructor, all coursework is to be done by the student working alone. Violations of the rules
will not be tolerated.
