Computer Vision (1905322)
Course syllabus (PDF)
Lecture notes*
Week 1: Introduction to Computer Vision
Week 2: Image Formation – cameras and optics, light & color
Week 3: Image Filtering – spatial and frequency domain filtering
Week 4: Image Filtering – image pyramids & applications
Week 5: Feature Detection and Matching – gradient and edges, points & corners
Week 6: Feature Detection and Matching – local image features & texture analysis
Week 7: Feature Detection and Matching – feature matching & Hough transform
Week 8: Feature Detection and Matching – model fitting & RANSAC
Week 9: Multiple Views and Motion – Stereo vision, epipolar geometry & structure from motion
Week 10: Multiple Views and Motion – feature tracking & optical flow
Week 11: Machine Learning – clustering & classification
Week 12: Deep learning Basics – clustering & classification
Week 13: Object Detection – bag of features, sliding window detection & scene recognition
Week 14: Object Detection – Semantic Segmentation, Instance Segmentation & 3D Understanding
Week 15: Course wrap-up and project presentations
Midterm exam marks click here (NEW)
*Textbook: Computer Vision: Algorithms and Applications, 2nd edition by Richard Szeliski (PDF)
Programming environment: MATLAB – Mathworks (R2023b Release)