Office: N-232
Email: zubair.irshad@sse.habib.edu.pk
Office Hours: Wed: 2-4 PM
Website: zubairirshad.com
Have you ever wondered how a machine or computer is made capable of understanding, interpreting, and giving semantics to an image/video? Have you ever thought about how image/video could be used to automate processes in a wider application domain ranging from industry to biomedicine? The answer lies in image processing and computer vision which plays a central role in all of these.
This course is a continuation of EE451 Digital Image Processing and is developed for Electrical Engineering and Computer Science students. Computer Vision has emerged as a revolutionary field that employs Image Processing, Pattern Recognition, and Machine Learning to imitate human vision in order to automate processes in a wide range of real-world applications.
The ever so rapid growth and hence increasing applications have made Computer vision ubiquitous and it, along with Artificial Intelligence, is transforming the way humans approach their daily routine life. In this course, the aim is to explore computer vision from both foundational and modern learning-based perspectives, covering fundamentals i.e. image formation/processing, deep learning for vision, 3D geometry/multi-view methods, motion estimation, and emerging areas such as radiance fields, generative models, and vision–language systems. The course emphasizes practical understanding through assignments and a semester-long project.
The course aims to equip students with:
| Week | Topic(s) | Reading(s) | Remarks |
|---|---|---|---|
| Foundations of Vision I: Image Formation and Processing | |||
| Week - 1 January 12 – 16, 2026 |
Intro to Course, Overview of Computer Vision Math/Linear Algebra Review |
- | - |
| Week - 2 January 19 – 23, 2026 |
Geometric Image Formation, Photometric Image Formation |
Chapter 5, 6, 7 | Assignment 0 released |
| Week - 3 January 26 – 30, 2026 |
Foundation of Image Processing: Linear Filters, Systems and Convolutions |
Chapter 15, 16 | Assignment 0 due Assignment 1 released |
| Week - 4 February 02 – 06, 2026 |
Derivative, Edges and Lines | - | - |
| Deep Learning | |||
| Week - 5 February 9 – 13, 2026 |
Learning to See | Chapter 9, 11 | Assignment 1 due |
| Week - 6 February 16 – 20, 2026 |
Neural Networks, How to do good research |
Chapter 12, 13, 14 | Assignment 2 released |
| Week - 7 February 23 – 27, 2026 |
Convolutional Neural Networks | Chapter 24 | Project Proposal Due |
| Week - 8 March 2 – 6, 2026 |
Transformers Continued | Chapter 26 | |
| Week - 9 March 9 – 13, 2026 |
Representation Learning Midterm March 11, 2026 |
Chapter 30 | Assignment 2 due Assignment 3 released |
| Foundations of Vision II: 3D, Geometry and Motion | |||
| Week - 10 March 16 – 20, 2026 |
3D Geometry, Camera Calibration | Chapter 38, 39, 40 | Project detailed literature review due |
| Week - 11 March 23 – 27, 2026 |
Multi-view Geometry, Learning-based 3D Estimation Holiday March 23 (Pakistan Day) |
Chapter 43, 44 | Assignment 3 due Assignment 4 released |
| Week - 12 Mar 30 – Apr 03, 2026 |
Structure from Motion, Motion Estimation, Optical Flow, and Tracking |
Chapter 44, 46, 47, 48, 49 | - |
| Advanced Frontiers | |||
| Week - 13 April 6 – 10, 2026 |
Project Progress Demos, Object Recognition |
Chapter 50 | Assignment 4 due |
| Week - 14 April 13 – 17, 2026 |
Radiance Fields, Generative Models | Chapter 32, 33, 45 | - |
| Week - 15 April 20 – 24, 2026 |
Vision-and-Language | Chapter 51 | - |
| Week - 16 April 27 – 30, 2026 |
Final Project Demos | - | Project Finals and Paper due |
| Component | Weight |
|---|---|
| Class Participation + Quizzes | 10% |
| Assignments (4) | 35% |
| Midterm Exam | 20% |
| Project and Paper | 35% |
The course project accounts for 35% of the final course grade. The project is designed to evaluate your ability to define a problem, make steady technical progress, and clearly communicate your ideas and results.
It consists of three required components:
| Component | Weight |
|---|---|
| Project Proposal | 10% |
| Literature Review | 5% |
| Progress Report | 10% |
| Final Report & Presentation | 10% |
The proposal and progress report will be assessed by the course instructor.
