CSE327: Computer Vision

http://www.cs.stonybrook.edu/~cse327

Instructor: Prof. Dimitris Samaras

Fall 2017: Tuesday and Thursday 5:30-6:50, CS 2311

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 this course will cover include:

 

1.     Image Formation

Basic facts about light

Anatomy of a camera

Matting

2.     Image Noise
Modeling image noise

Convolution
Smoothing images

3.     Image Features
Edge Features
Point Features, Corners
The Hough Transform

4.     Model Fitting _
Lines, Curves
Deformation

RANSAC

5.     Texture

Scale

Orientation

Image Pyramids

6.     Illumination

Shading, Shadows,

Reflectance properties

7.     Perspective Projection

Homogeneous Coordinates

Image Warping

Mosaics

8.     Multiple View Geometry
 Stereo Viewing and Reconstruction

        3D Range Scanning

9.     Image Patches

The SIFT descriptor

Template Matching

PCA for Image Patches

10.  Motion

Motion Capture

Tracking in 2D and 3D

11.  Segmentation

Grouping,

Nearest Neighbors

12.  Object Recognition

Object representation

Classifiers

Object Categories  

13.  Deep Learning

Convolutional Neural Networks

Architectures

Applications

14.  Deep Learning Practice

Pre-Training

Data Augmentation

Recurrent Neural Network

Action Recognition

 

 

Intended Audience:

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. 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 3 homeworks, a final project, two midterms and 3 10 min quizes. Homeworks will be 40%, the project 30%, and the exams 35%. 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.  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.

Midterm date:  October 22nd and November 28th,  2017.
You can have one sheet of paper with notes in the midterms.

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)

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, please refer to the academic judiciary website at http://www.stonybrook.edu/uaa/academicjudiciary/ _

__________________________________________________________________________________

____________ Adopted by the Undergraduate Council September 12, 2006 __“______

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:

·       D. Samaras, Tel. 631-632-8464
email: samaras@cs.sunysb.edu

Office Hours: Tue., 3pm to 4.30pm Thu 2pm to 3.30, or by appointment
                         New Computer Science room 263

·       TA: Zhibo Yang,

Email: zhibo.yang@stonybrook.edu

Office Hours: Monday 2pm to 3:30pm, or by appointment. Old CS Room 2127