We'll use Canvas' discussion and messaging features for course-related communication.
Labs and inclass exercises will be submitted through course canvas site.
Computer vision notes are available here. The course covers a selection of topics from these notes.
Additional notes are available here.
This graduate-level course offers an introduction to computer vision, with emphasis on both theoretical foundations and real-world applications.
At its core, computer vision is concerned with processing and analyzing digital images to extract meaningful information about the physical world. Applications range from reconstructing 3D scene structure from photographs, to recognizing people in images, to identifying actions in video sequences. Beyond these general tasks, computer vision plays a vital role in specialized domains such as medical imaging (e.g., analyzing CT or MRI scans), remote sensing (e.g., monitoring ecosystems through satellite imagery), and the entertainment and gaming industries.
Despite its successes, computer vision remains a challenging field. Digital images are essentially arrays of pixels, and inferring structure, semantics, or dynamics from them is non-trivial. Recent advances in machine learning—particularly deep learning—have transformed the field by enabling algorithms that can effectively “learn to see” from large collections of example images and videos. To reflect this, the course will also introduce modern machine learning methods that have become central to contemporary computer vision research and applications.
This course blends short lectures with interactive paper readings and discussions. The lectures will give you the background you need to dive into research papers, understand their methods, and try out key ideas yourself. In most classes, the first 45 minutes will focus on a core topic, and the rest of the time will be spent presenting papers and exploring them together in discussion.
We’ll cover topics such as:
These topics will give you the tools and intuition to make sense of the papers we read and to actively engage with the exciting questions in computer vision research.
The course assumes that students are comfortable with statistics, basic linear algebra, and programming.
We will be using Python for the programming part of this course. For Python, I recommend the Anaconda distribution, which comes pre-loaded for nearly all the packages that we will be using in this course. Of course you are welcome to use any variant/distribution of Python that suits you.
The course also assumes that students are willing to read and comprehend large volumes of technical papers. Furthermore, that students have some experience with technical report writing.
Ontario Tech University’s academic calendar that lists important dates (and deadlines) is available at here.
The list of assigned papers will be available after the first week of classes. Please check the course website for details.
The midterm will cover the topics discussed in the following set of notes. This list will be updated after each lecture.
Find a collection of computer vision papers at https://github.com/jbhuang0604/awesome-computer-vision. The paper is organized in topics. Please find papers in topics that interest you. Please find at least five papers in two different areas. At least three of the five papers should be recent.
See course canvas for more instructions.
Each student will be assigned recent papers to read and present. The student will be responsible for leading the discussion for this paper. Each student may be assigned to present multiple papers.
The course project is an independent exploration of a specific problem within the context of this course. A project can be implementation oriented—where a student implements a computer vision system—or application oriented—where a student attempts to solve a problem (of suitable difficulty) by applying machine learning techniques. The project topic will be selected in consultation with the instructor.
Project grade will depend on the ideas, how well you present them in the report, how well you position your work in the related literature, how thorough are your experiments and how thoughtful are your conclusions.
Course project is typically an individual effort.
Projects must be related to computer vision theory, methods, and systems. A project that simply uses a pre-trained deep learning model, say YOLO or ImageNet to solve some larger “task” is not appropriate. Such a project simply applies a pre-built system to the task at hand. I want us to have an opportunity to implement computer vision systems that underpin all these different applications.
Possible topics are:
In many cases it is difficult to deal with real cameras and hardware. In these situations it is possible to implement and evaluate your algorithms using simulated data. E.g., you can use a game engine to simulate traffic images captured at a road intersection.
For your final project write-up you must use ACM SIG Proceedings Template (available at the ACM website). Project report is at most 12 pages long, plus extra pages for references. Your report must of “publishable quality,” i.e., no typos, grammar error.
The final deadline for project report submission is 11th of December, midnight EST. This is a firm deadline. You will incur a penalty of 40% if you do not meet this deadline. These strict rules mimic conference submission process:
You will find the following computer vision books useful.
Following books are good resources for machine learning, especially deep learning
These resources will not only help you understand the assigned papers. These resources may prove invaluable for your course projects.
Here you’ll find a number of tutorials showcasing Python use in machine learning. I strongly recommend that you become comfortable with the following four Python packages/environment: