Table of Contents

General Knowledge

G1. Books

• Foundations of Computer Vision, Antonio Torralba, Phillip Isola, and William Freeman
• Computer Vision: Algorithms and Applications, Richard Szeliski
• Computer Vision: Models, Learning, and Inference, Simon Prince

G2. Lectures and Tutorials

• Stanford CS231n: Deep Learning for Computer Vision, Fei-Fei Li and many other contributors
  • Lecture videos (2025) are available in YouTube.
• Stanford CS231A: Computer Vision, From 3D Perception to 3D Reconstruction and Beyond, Silvio Savarese and many other contributors
• Stanford CS131: Computer Vision: Foundations and Applications, Juan Carlos Niebles and Adrien Gaidon
• Univ. of Tubingen ML-4360: Computer Vision, Andreas Geiger
  • Lecture videos (2023) are available in YouTube.
  • Note) Andreas Geiger’s other lectures are also quite useful such as Deep Learning and Math for DL.
• Understanding the In-Camera Rendering Pipeline and the role of AI/Deep Learning (ICCV 2023 tutorial), Michael S. Brown

G3. Resources and Datasets

• Awesome Computer Vision, Jia-Bin Huang
• CVPapers (Computer Vision Resource)
  • It includes conference paper index, lists of open implementations and datasets.
• Computer Vision Online
  • It includes software, datasets, groups, books, events, jobs on computer vision.
• CVonline, Robert Fisher
• VisionBib.com (Computer Vision Information Pages), Keith Price
  • Calendar of Computer Image Analysis, Computer Vision Conferences

G4. OpenCV