Instructor:Dimitris Samaras

First Class: Tuesday September 9, 2003

www.cs.sunysb.edu/~samaras/cse615.html

Recent advances in recording, storing and processing large amounts of visual data have focused attention in a number of interesting problems in the junction of Machine Learning and Computer Vision. Traditionally, computer vision research has been aiming to find appropriate models to describe the physics of natural scenes. In this course we will focus on how machine learning and inference can be used to derive or augment such models by using previously recorded visual data, and examine recent results in the field.

We will cover **computer vision topics** in i) object detection and
segmentation, ii) object tracking, iii) object recognition, iv) texture
analysis and synthesis v) scene analysis and inference.

Some of the **machine learning methods **we will be looking at include:
Bayesian networks, graphical models, the EM algorithm, Hidden Markov Models,
Markov Random Fields, markov chain monte carlo methods, particle filtering,
PCA, ICA, kernel based methods, Support Vector Machines, boosting and bagging,
graph cuts. We will mostly look at this methods in the context of the computer
vision problems mentioned above, with a brief theoretical introduction
as necessary. However, many of the ideas and techniques used here are also
used in other areas of AI (e.g. robotics, natural language understanding,
learning).

The intended **audience** for this course is graduate students with
a background in computer vision, image processing, AI, and computer
graphics (esp. of the image based type!). This is NOT an introductory course
although due to an error in SOLAR the course is listed as "Introduction
to Computer Vision". Students from any department are welcome as
long as they have the necessary programming skills for the project, as
well as a mathematical background that includes linear algebra, calculus,
and basic probability.

The course will be **organized **as a combination of lectures by
the instructor and paper presentations by the students. Each student will
have to do two paper presentations, as well as a final programming project.
The project should be at the frontier of current research (although not
necessarily move the frontier forward). There will be no final exam, however
in the beginning of some of the presentations students will be asked
to answer a short easy question on the paper that will be presented (to
ensure that everyone has read it).

**Grading** will be: i) 65% Project: Project proposals will be due
on October 21. Mid term demo will be on November 20. Final demo will be
on December 18-19. ii) 20% Presentations. iii)15% Class participation

With **permission from the instructor** students who do not wish
to do the project can take the course for 0/1 credit by registering for
the seminar in Computer Vision CSE 656.

**References** will be mostly research papers as well as the following
textbooks:

Forsyth and Ponce, Computer Vision -- A Modern Approach

Trucco and Verri, Introductory Techniques for 3-D Computer Vision

Duda and Hart and Stork, Pattern Classification

Tom Mitchell, Machine Learning

**Contact info:**

D. Samaras, Tel. 631-632-8464

email: samaras@cs.sunysb.edu

Office Hours: Wed 1:30pm to 3pm, Thu 4 to 5:30pm
or by appointment

Computer Science room 2429