Computer Vision

Part I: Introduction to Computer Vision

Defines the notion of computer vision, the progress made in this discipline in recent decades, current challenges, successful applications, and its limitations. We also discuss selected biological vision mechanisms as an inspiration to create better computer vision solutions.

Part II: Digital Image Processing

Explains the basics of signal processing from a computer vision perspective. This part includes image formation, image acquisition, understanding and effective use of color (and in general multi-wavelength) information, and image processing (filtering and segmentation).

Part III: Visual Recognition

Focuses on the automatic recognition of patterns. It covers feature extraction and selection, texture descriptors, Bayesian inference, classification, and decision making. In this part, we will also discuss how these tasks can be solved using deep learning techniques, analysing convolutional neural networks and selected modern neural network-based architectures. Finally, we will discuss the reliability of modern deep learning-based, generative, and image-to-image translation models.

Part IV: Multiple-view Analysis

Considers multiple-view and geometry topics in vision: motion analysis, including object tracking, projective geometry, camera geometric model, camera calibration, and 3D reconstruction.

Class Information

Lecture

M/W/F 12:50 PM - 1:40 PM

Location

356A Fitzpatrick Hall of Engineering

Slack

#cse-40535-sp26

Instructor

Instructor

Arturo Miguel Russell Bernal (arussel8@nd.edu)

Office Hours

M 2:00 PM - 4:00 PM, and by appointment

Office Location

326C Cushing Hall of Engineering

Teaching Assistants

Graduate Teaching Assistant

TBD (tbd@nd.edu)

Office Hours

TBD TBD, and by appointment

Office Location

TBD

Students with Disabilities

Any student who has a documented disability and is registered with Sara Bea Accessibility Services should speak with the professor as soon as possible regarding accommodations. Students who are not registered should contact Sara Bea Accessibility Services.

Academic Honesty

Any academic misconduct in this course is considered a serious offense, and the strongest possible academic penalties will be pursued for such behavior. Students may discuss high-level ideas with other students, but at the time of implementation (i.e., programming), each person must do his/her own work. Use of the Internet as a reference is allowed but directly copying code or other information is cheating. It is cheating to copy, to allow another person to copy, all or part of an exam or a assignment, or to fake program output. It is also a violation of the Undergraduate Academic Code of Honor to observe and then fail to report academic dishonesty. You are responsible for the security and integrity of your own work.

Policy on Generative AI

We are big advocates of the responsible use of modern AI tools, including generative AI models that are in the headlines (this is a computer vision course, after all). We will have opportunities to use the output of these systems in assignments, and we will discuss their usefulness for solving computer vision problems in class. But you must not use generative AI to simply "do your work for you" (e.g., generate ready-to-submit homework answers, or code snippets). This is not what we would call "responsible use" and it is harmful to your long-term learning goals. The prose generated by ChatGPT is spelled and punctuated correctly and has proper grammar, but it frequently concocts false information, and supports its arguments with very shallow reasoning that relies on description rather than inference. Use it as a "brainstorming buddy" (ChatGPT's confabulations can be very inspirational as they may take you to ideas you have never thought about). Or use it to simply correct your grammar / language. But do not cheat (see the the Academic Honesty section of this page).

Classroom Recording

Notre Dame has implemented a classroom recording system. This system allows us to record and distribute lectures to you in a secure environment. You can watch these recordings on your computer, tablet, or smartphone. The recordings can be accessed via a request made to the instructor.

Because the instructor reserves the right to record in the classroom on select occasions, your questions and comments may be recorded. (Video recordings typically only capture the front of the classroom.) If you have any concerns about your voice or image being recorded, please speak to me to determine an alternative means of participating. No content will be shared with individuals outside of your course without your permission except for faculty and staff that need access for support or specific academic purposes.

These recordings are jointly copyrighted by the University of Notre Dame and your instructor. Posting them to other websites, including YouTube, Facebook, Vimeo, or elsewhere without express, written permission may result in disciplinary action and possible civil prosecution.