CSE527: Introduction to Computer Vision

http://www.cs.sunysb.edu/~cse527

Instructor: Prof. Dimitris Samaras

Fall 2018: Tue-Thu 5:30 – 6:50 in Javits 103 Computer Science

Course Syllabus

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:

 

 

1.     Image Formation

Basic facts about light

Anatomy of a camera

Matting

2.     Image Noise
Modeling image noise

Convolution
Smoothing images

Image Pyramids

3.     Image Features
Point Features, Corners
Edge Features

Scale

Orientation

4.     Model Fitting
Lines, Curves
The Hough Transform

Deformation

RANSAC

5.     Perspective Projection

Homogeneous Coordinates

Image Warping, Mosaics

6.     Object Recognition

Object representation

PCA for Image Patches

Classifiers

Object Categories  

7.     Deep Learning

Convolutional Neural Networks

Architectures

Applications

8.     Deep Learning Practice

Pre-Training

Data Augmentation

9.     Multiple View Geometry
Stereo Viewing and Reconstruction

       3D Range Scanning

10.  Illumination

Shading, Shadows,

Reflectance properties

11.  Deep Generative Models

Autoencoders, VAEs,

Generative Adversarial Networks

12.  Motion

Motion Capture

Tracking in 2D and 3D

Recurrent Neural Networks

Action Recognition

13.  Segmentation

Grouping, SuperPixels

Nearest Neighbors

UNET,

Semantic Segmentation

14.  Big Data

Annotated Data Sets

Crowdsourcing

Weakly Supervised and

Unsupervised Learning

 

Intended Audience:

This course is intended for graduate students with interests in all areas of  Visual Computing and Machine Learning, 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).

 
Grading:

There will be homeworks, a midterm and a final. Homeworks will be 45%, the midterm 20%, and the final 35%. Weights are approximate and subject to change. You are expected to do homeworks (4 or 5) 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! There will be 3 free late dates for the semester. After that there will be 10% penalty per day.

·       You can do a project instead of the final. 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 studying for the final. Two people projects will be scaled accordingly.

Midterm 1 date:  Oct 18th 2018

Final date: Dec 13th 2018. Projects due Dec 18th 2018.

You can have one sheet of paper with notes in the exams.

Textbook:

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)

Computer Vision: Models, Learning, and Inference by S. Prince, Cambridge Univ. Press, 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

 

Academic misconduct policy:

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/

Disability note:

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.

Contact info:
We will be using Piazza for class discussion. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza so everyone may benefit from the answers.
Our class page is https://piazza.com/class/jlb7jr8lep01jl

·       D. Samaras, Email: samaras@cs.sunysb.edu

Office Hours: Tue., Thu 4 - 5pm to 3.30, or by appointment, in room NCS 263

·       TA: Fan Wang, Email: fanwang1@cs.stonybrook.edu

Office Hours: Mon. 11:00 - 12:00pm, Wed. 3:00 - 4:00pm in room NCS 140