ECE627 - Computer Vision

Course Description

In the simplest terms, computer vision is the discipline of "teaching machines how to see."  This course covers advanced research topics in computer vision. This class will prepare graduate students in both the theoretical foundations of computer vision as well as the practical approaches to building real Computer Vision systems. This course investigates current research topics in computer vision with an emphasis on recognition tasks and deep learning. We will examine data sources, features, and learning algorithms useful for understanding and manipulating visual data. Class topics will be pursued through independent reading, class discussion and presentations, and state-of-the-art projects.

The goal of this course is to give students the background and skills necessary to perform research in computer vision and its application domains such as robotics, healthcare, and graphics. Students should understand the strengths and weaknesses of current approaches to research problems and identify interesting open questions and future research directions. Students will hopefully improve their critical reading and communication skills, as well.


When and Where:

Syllabus - Schedule

The lecture slides are provided SUPPLEMENTARY to class material/notes, not as a replacement! You are still responsible for attending the course lectures.

Slides will be posted AFTER the lectures. 


Grading Scheme

*The instructor reserves the right to make minor changes to the above grade scheme after clarifying it to the students. In addition, the instructor reserves the right to marginally adjust grades based on classroom attendance, once this has been clarified to students.

Recommended textbooks

Course Semester Project

There are two options for this project:


For each project you will have to turn in:


Turn in a writeup paper describing your problem and approach. It should include the following:

In addition, the project will also be evaluated based on:

Project Ideas:

Project Examples from the 2020 class

Image-based Control of UAV actions

By Andreas Anastasiou

Mask detection for combating viral disease spread

By George Plastiras

What you should take from this Class

Course Policies

Unless otherwise stated by the course instructor, homework and coursework that is to be completed outside contact time with the instructor must be done on an individual basis. This means that copying material from others is considered to be plagiarism

Plagiarism is not permitted, and is also not tolerated

Students caught plagiarizing somebody else’s work will receive a significant reduction on the related assignment’s mark, sometimes even down to zero. Students caught assisting others in plagiarizing will be penalized in the same way.