Course - Computer Vision MT25
- Course webpage (old)
- Lecture notes (from previous year)
- 1, Introduction
- 2, Image enhancement
- 3, 2D Fourier transforms and applications
- 4, Image restoration
- 5, Matching, indexing, and search
- 6, Image classification
- 7, Convolutional networks
- 8, Transformer networks for images
- 9, Visualisation and understanding
- 10, Object detection
- 11, Image segmentation
- 12, Videos
- 13, Tracking
- 14, Camera models and triangulation
- 15, Multiple view geometry
- 16, Generative models
- 17, Representation learning
- 18, Unsupervised computer vision
- 19, Vision and language
- 20, Ethics and privacy
- My notes here are based primarily on the slides above, written by Prof. Christian Rupprecht.
- Lecture recordings
- Practicals
- Other courses this term: [[Courses MT25]]U
Notes
- [[Notes - Computer Vision MT25, Image representation]]U
- [[Notes - Computer Vision MT25, Sampling and reconstruction]]U
- [[Notes - Computer Vision MT25, Subsampling and upsampling]]U
- [[Notes - Computer Vision MT25, Image transformations]]U
- [[Notes - Computer Vision MT25, Homogenous coordinates and homographies]]U
- [[Notes - Computer Vision MT25, Convolutions]]U
- [[Notes - Computer Vision MT25, Filtering]]U
- [[Notes - Computer Vision MT25, Fourier transform]]U
- [[Notes - Computer Vision MT25, Image restoration]]U
- [[Notes - Computer Vision MT25, Correspondences]]U
- [[Notes - Computer Vision MT25, Scale-invariant feature transform]]U
- [[Notes - Computer Vision MT25, Image classification]]U
Related notes
The majority of [[Course - Machine Learning MT23]]U is relevant, but especially:
- [[Notes - Machine Learning MT23, Classification]]U
- [[Notes - Machine Learning MT23, Clustering]]U
- [[Notes - Machine Learning MT23, Convolutional neural networks]]U
- [[Notes - Machine Learning MT23, Cross-entropy loss]]U
- [[Notes - Machine Learning MT23, Generative models]]U
- [[Notes - Machine Learning MT23, Linear regression]]U
- [[Notes - Machine Learning MT23, Logistic regression]]U
- [[Notes - Machine Learning MT23, Matrix calculus]]U
- [[Notes - Machine Learning MT23, Maximum likelihood principle]]U
- [[Notes - Machine Learning MT23, Naïve Bayes classifiers]]U
- [[Notes - Machine Learning MT23, Neural networks]]U
- [[Notes - Machine Learning MT23, Perceptrons]]U
- [[Notes - Machine Learning MT23, Principal component analysis]]U
- [[Notes - Machine Learning MT23, Support vector machines]]U
- [[Notes - Machine Learning MT23, k-means clustering]]U
- [[Notes - Machine Learning MT23, k-nearest neighbours]]U
Surprisingly, there is a small amount of overlap with the content covered in
- [[Notes - Complex Analysis MT23, Extended complex plane]]U
- [[Notes - Complex Analysis MT23, Möbius maps]]U
when it comes to homogenous coordinates and the projective line model of the complex plane.
Problem Sheets
- Sheet 1 (old), [[Problem Sheet - Computer Vision MT25, I]]?
- Sheet 2 (old), [[Problem Sheet - Computer Vision MT25, II]]?
- Sheet 3 (old), [[Problem Sheet - Computer Vision MT25, III]]?
- Sheet 4 (old), [[Problem Sheet - Computer Vision MT25, IV]]?