Computer Vision - University of The Punjab

Welcome to the Computer Vision course page! Computer Vision is a field of AI that focuses on enabling machines to interpret and understand the visual world. In this course, you will study both the theoretical foundations and practical applications of computer vision techniques, exploring how algorithms can process, analyze, and extract meaningful information from images. This course covers the theory and practice of computer vision. Dive into image processing, neural networks, object recognition, and advanced techniques such as deep learning, 3D vision, and motion analysis.

Lectures

Lecture	Topic	Readings	Tutorials & Evaluations
1	Introduction	Ch 1	Setup a Python environment for CV
2	Looking at Images + Computer Vision and Society	Ch 3, 4
3	Image Formation and Lenses	Ch 5, 6	How to make a pinhole camera Tutorial 1: Introduction
4	Cameras as Linear Systems + Color	Ch 7, 8 See first 8 slides of this lecture on matrix and vector calculus. You may also see the video explanation of those slides. See the Wiki page on matrix calculus from which we will use denominator-layout notation. See pages xxvii, xxviii, xxx, and xxxi of the FoCV textbook.	Quiz 1, Solution Tutorial 2: Color Spaces
5	Introduction to Machine Learning	Ch 9 Slides	Tutorial 3: Linear Regression
6	Gradient-based Learning + Generalization	Ch 10, 11 Slides, Slides
7	Neural Networks	Ch 12, 13 Slides	Tutorial 4: Neural Networks using PyTorch: Recognition of Handwritten Digits
8	Linear Image Filtering	Ch 15	Quiz 2, Solution Tutorial 5: Convolution
9	Blur Filters	Ch 17
10	Image Derivatives	Ch 18	Assignment 1 Deadline: 11:59 pm, November 27
11	Image Sampling	Ch 20, 21	Quiz 3
12	Filter Banks	Ch 22
13	Image Pyramids	Ch 23	Assignment 2
14	Convolutional Neural Networks	Ch 24
15	Transformers	Ch 26
16	Perceptual Grouping	Ch 31
17	Dataset Bias and Robust Learning	Ch 35, 36
18	Transfer Learning and Adaptation	Ch 37	Assignment 3
19	Representing Images and Geometry	Ch 38	Quiz 4
20	Camera Modelling and Calibration	Ch 39
21	Stereo Vision	Ch 40
22	Homographies	Ch 41	Assignment 4
23	Single View Metrology	Ch 42	Quiz 5
24	Learning to Estimate Depth from a Single Image	Ch 43
25	Multiview Geometry and Structure from Motion	Ch 44	Assignment 5
26	Motion Estimation	Ch 46, 47	Quiz 6
27	Optic Flow	Ch 48, 49
28	Object Recognition	Ch 50
29	Conclusion

Computer Vision (EC 331) - Department of Computer Science

Lectures

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