Lecture 9: Image Data Processing (Part I)

(Last updated: Feb 24, 2026)

This lecture introduces the theory for image data processing, including the convolution operation, image filtering, and convolutional neural network.

Check the GenAI usage policy if you are using the course materials with GenAI for self-study and fact-checking.

Preparation

Read the required course readings.

Lecture

Below are the slides:

• Slides for Lecture 9-1: Image Data Processing (Image Filtering)
• Slides for Lecture 9-2: Image Data Processing (Convolutional Neural Network)

Required Course Readings

• Section 5.4 (Convolutional neural networks) in book Computer Vision: Algorithms and Applications (Szeliski, 2022). You can ignore the following subsections: U-Nets and Feature Pyramid Networks, Mobile networks, 5.4.4 Model zoos, 5.4.6 Adversarial examples, and 5.4.7 Self-supervised learning.

Optional Course Readings

• Section 5.3 (Deep neural networks) in book Computer Vision: Algorithms and Applications (Szeliski, 2022).

Additional Resources

The following documentations explains the convolutional layer in great details:

• Dumoulin, V., & Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285.

The following website has an interactive visualization for understanding image filtering:

• Image Kernels Explained Visually

The following website has an interactive visualization for understanding Convolutional Neural Network:

• CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization

The following website shows a live training demo using the MNIST dataset:

• ConvNetJS MNIST demo

Below are tutorials of Convolutional Neural Network:

• Convolutional Neural Networks tutorial (more technical)
• Convolutional Neural Networks tutorial (more intuitive)

The following webpage has many examples of Computer Vision applications:

• The 100 Most Popular Computer Vision Applications in 2023