Instructor: Prof. Dimitris Samaras
Spring
2021: Tuesday and Thursday 4:45 – 6:05 online on Zoom (Blackboard)
The aims of this course are
to provide an understanding of the fundamentals of Computer Vision and to give
a glimpse in the state-of-the-art, at a moment when the field is achieving
"critical mass" and has significant commercial applications. Apart
from basic theory we will look at applications of Computer Vision in Robotics,
Graphics and Medicine. Topics in this course:
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Basic
facts about light Anatomy
of a camera Matting
Convolution Image
Pyramids
Scale
Orientation
Deformation RANSAC
Homogeneous
Coordinates Image
Warping, Mosaics
Object
representation PCA
for Image Patches Classifiers Object
Categories |
Convolutional
Neural Networks Architectures Applications Pre-Training Data
Augmentation
3D Range Scanning
Grouping,
SuperPixels Nearest
Neighbors UNET,
Semantic
Segmentation
Motion
Capture Tracking
in 2D and 3D Recurrent
Neural Networks Action Recognition
Shading,
Shadows, Reflectance properties
Annotated Data Sets Crowdsourcing Weakly Supervised and Unsupervised Learning |
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This course is intended for
undergraduate students with interests in all areas of Visual Computing, such as
Computer Vision, Computer Graphics, Visualization, Biomedical Imaging,
Robotics, Virtual Reality, Computational Geometry, Optimization, Deep
Learning, HCI. Prerequisites include a
foundation in Linear Algebra and Calculus, and the ability to program. We will
be programming in Python (OpenCV, NumPy, SciKit).
There will be 5-6 homeworks,
a final, a midterm and 4 10 min quizes. You can do an optional project instead
of the last two homeworks. Homeworks will be 60%, and the exams 40%. The worse
of quizzes will be discarded. Weights are approximate and subject to change.
You are expected to do homeworks by yourselves. Even if you discuss them with your
classmates, you should turn in your own code and write-up. Do not share
your code! Final projects can be done by
one or two people. Two people projects will be scaled accordingly. There will
be 3 free late dates for the semester. After that there will be 10% penalty per
day.
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You can do a project instead of the final homework. Projects
will be done in up-to 2 people teams, and will require a significant
programming and documentation effort. This will probably be much more work
than doing the final homework. Two people projects will be scaled accordingly.
Midterm date: March 23rd, 2021
You can have one sheet of paper with notes in the midterms.
Computer Vision: Algorithms
and Applications by Richard Szeliski (2010) Main text, available online.
Computer Vision: A Modern
Approach by David Forsyth and Jean Ponce (2012)
Introductory Techniques for
3-D Computer Vision by E. Trucco and A. Verri, (1998)
Readings from these books and
notes for all topics will be posted on blackboard
Don't cheat. Cheating on
anything will be dealt with as academic misconduct and handled accordingly. I
will not spend a lot of time trying to decide if you actually cheated. If I
think cheating might have occurred, then evidence will be forwarded to the
University's Academic Judiciary and they will decide. If cheating has occurred,
an F grade will be awarded. Discussion of assignments is acceptable, but you
must do your own work. Near duplicate assignments will be considered cheating
unless the assignment was restrictive enough to justify such similarities in
independent work. Just think of it that way: Cheating impedes learning and
having fun. The labs are meant to give you an opportunity to really understand the
class material. If you don't do the lab yourself, you are likely to fail the
exams. Please also note that opportunity makes thieves: It is your
responsibility to protect your work and to ensure that it is not turned in by
anyone else. No excuses! The University has a relevant policy:
“Each student must pursue his
or her academic goals honestly and be personally accountable for all submitted
work. Representing another person's work as your own is always wrong. Any
suspected instance academic dishonesty will be reported to the Academic
Judiciary. For more comprehensive information on academic integrity, including
categories of academic dishonesty, refer to the academic judiciary website at
http://www.stonybrook.edu/uaa/academicjudiciary/
If you have a physical,
psychological, medical or learning disability that may impact on your ability
to carry out assigned course work, I would urge that you contact the staff in
the Disabled Student Services office (DSS), Room 133 Humanities, 632-6748/TDD.
DSS will review your concerns and determine, with you, what accommodations are
necessary and appropriate. All information and documentation of disability is
confidential.
Zoom info is on blackboard and piazza.
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D. Samaras, Email: samaras@cs.stonybrook.edu
Office Hours:
Tue., 2.30-3.30pm, Thu 6:30-7:30pm or by appointment, on zoom or rm NCS 263
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TA: Qiaomu Miao,
Email: qiamiao@cs.stonybrook.edu
Office Hours: Friday 10:00 am-11:30am on zoom