Interactive class notebooks for ECE4076/5176 Computer Vision.
ECE4076/5176 is a computer vision unit at Monash University, covering both classical computer vision and modern deep learning methods. These notebooks are in class activities used alongside pre-recorded lectures covering more detailed material, and paired with laboratory sessions where students implement related concepts.
You may want to brush up on some python skills before attempting these, using this notebook.
- Week 1: Image handling and basic manipulation, high dimensional signals
- Week 2: Image filtering, Difference of Gaussians, Keypoint Detection, Patch Matching using SSD
- Week 3: Invariances and image transformations, assymetric feature matching
- Week 4: Camera models and homography estimation using RANSAC
- Week 5: Camera projection and vanishing points and lines
- Week 6: Multiple view geometry, space carving
- Week 7: Clustering and gaussian mixture models
- Week 8: Logistic regression
- Week 9: Understanding gradient descent
- Week 10: Object detection with Alexnet and Resnets
- Week 11: Object recognition with RCNNs and YOLO
- Week 12: Image segmentation with U-Nets
There is some additional material we unfortunately don't have time to cover in class in the bonus content folder .
- Vision transformers
- Variational autoencoders
- Generative Adversarial Networks
- Clip
- Diffusion models
Contributors and Acknowledgements:
- Michael Burke
- Mehrtash Harandi
- Week 5 is based on a nice example from Tom Drummond