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---
title: Lecture Notes - AIS2204 Maskinsyn
categories: Module
---
# Chapter 1-2. Introduction and 3D Modelling (two weeks)
**Dates** 25-26 August + 1-2 September
| # | Session Notes | Reading | Keywords | Status |
|---|---------------|-------------------|-----------------------------|-----|
| 1 | [Introduction]() | Ma 2004:Ch 1 (Ch 2.1 and 2.3) | Practical matters. Software installation. Recap of linear algebra. | OK |
| 2 | [3D Modelling]() | Ma 2004:Ch 2, App A (SZ 2) | 3D modelling, motion | OK |
| 3 | [3D Objects in Python]() | Tutorials | Homogeneous co-ordinates. General Rotations. 3D Transformations in Python | OK |
| 4 | [3D Modelling Part II]() | | Velocity transformations. Recap. Questions. | To be adapted to class |
# Chapter 3. Image Formation (two weeks)
| # | Topic | Reading | Keywords | Status |
|---|---------------|-------------------|-----------------------------|------|
| 5 | [Image Formation]() | Ma 2004:Ch 3-3.3.1 (SZ 6) | projection, lens/camera | OK |
| 6 | [Camera Calibration]() | Ma 2004:Ch 3.3-3.3.3 | Calibration, Radial Distortion etc. | OK |
| 7 | [More Camera Mathematics]() | Ma 2004:Ch 3.3-3.4 | Radial Distortion, Tangential Distortion | OK |
**Dates** 8-9 and 15 September
# Chapter 4. Feature Tracking (three weeks)
(Last year's session numbers in parentheses.)
| # | Topic | Reading | Keywords | Status |
|---|---------------|-------------------|-----------------------------|----|
| 8 (new) | [Image Filters]() | | Convolution. Filters. Blurring. | OK |
| 9 (8-9) | [Corner Detection]() | Ma 2004:Ch 4.3, 4.A (SZ 4) | Calculate Gradient. Harris Feature Detector. | OK |
| 10 | [Tracking Features]() | Ma 2004:Ch 4-4.2 | Tracking of Features. Tracking of Edges. | OK |
| 11 (new) | [Project Tracker]() | | **new** Lecture [Multiscale Detection]() | OK |
| 12 (16) | [SIFT]() | | Feature Matching. Feature Descriptor. | OK |
| 13-14 | *Self-Study* | Continue with [Tracking Features]() | - | - |
| 15 (11) | Recap | | Status, review, repetition | |
| 16 (11) | [Edges]() | Ma 2004:Ch 4.4 | Canny, connected components, line fitting | |
+ 8 - 16 September
+ 9-12 - 22-23 and 29-30 September
+ 13-14 - 6-7 October - staff seminar - self-managed work only
+ 15-16 - 13-14 October - midterm - regular teaching
# Chapter 5. Projective Reconstruction (two weeks ?)
| # | Topic | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 17 | [Relative Pose]() | Ma 2004:Ch 5.1 | Triangulation. Relative Pose. Essential Matrix. |
| 18 | [Eight-point algorithm]() | Ma 2004:Ch 5.2 | Calculate Essential Matrix |
| 19 | [Study Technique]() | Ma 2004:Ch 5.1 | Proof reading. |
| 20 | [3D Reconstruction]() | Ma 2004:Ch 5.1-2 | |
| 21 | [Planar Scenes]()| Ma 2004:Ch 5.3 | |
| 22 | [Epipolar Geometry]()| Ma 2004:Ch 5.1-3 | |
1. Note from 2021:
[Study Technique]() should probably be done earlier in the semester
- In 2022 we have had fragments earlier, but this is still the first deep dive.
1. [Study Technique]() should may be needed earlier. We introduced some
fragments of this in 2022, but should possibly do more of it.
3. [Relative Pose]() is a little messy. It serves covers two things.
- triangulation is poorly covered in the textbook and the notes, but the students
need a recap from basic calculus
- triangulation is poorly covered in the textbook and the notes,
but the students need a recap from basic calculus
- the essential matrix is preparation for the next session.
4. Note from 2021: Generally, the preliminary steps of the exploratory exercises should have been
premade, to save time for the students.
5. Note from 2021: Algorithm implementation is difficult and require sample solutions
6. Examples with complete calculations
4. Generally, the preliminary steps of the exploratory exercises should
have been premade, to save time for the students.
We have made some improvements in 2022, but we should do more.
5. Algorithm implementation is difficult and require sample solutions.
Some have been added in 2022, but may have to be incorporated earlier
in the course.
6. We need more examples with complete calculations
# New. Machine Learning
| # | Topic | Reading | Keywords |
|----|---------------|-------------------|-----------------------------|
| 23-24 | [Neural Networks]() | Szeliski 2022 Chapter 5 | Training. Testing |
| 25 | [Statistics]() | | Evaluation, Standard Deviation |
| 26 |
+ Principles of Artificial Neural Networks
+ Graph Representation: Linear Combination + Non-Linear Activiation
+ Interpretation of Outputs
+ Loss Function
+ Optimisation Problem
+ Tensor Representation
+ Back-Propagation
+ Evaluation: Statistical Estimation and Hypothesis Test
+ Image recognition in PyTorch
+ Tutorials
# Last Week. Summary and Miscellanea
| # | Topic | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 23/2021 | [Distorted Space]() | Ma 2004:Ch 6.1-2 | |
# Other Material.
+ [Overview of Python Demoes](Python/Overview)
# Old Material.
| # | Topic | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 24/2021 | [Stratified Reconstruction]() | Ma 2005:Ch 6.3-4 | |
| 25/2021 | [Partial Scene Information]() | Ma 2005:Ch 6.5 | |
| 26/2021 | [Real World Reconstruction]() | Ma 2004:Ch 11 | |
| 27/2021 | [Continuous Motion]() | Ma 2004:Ch 5.4 | |
1. Ma 2004 Chapter 10. Partial Scene Knowledge
- This is referenced as a building block in Chapter 11.
1. Ma 2004 Chapter 11.4.
1. Ma 2004 Chapter 11.5. **Keywords** texture, visualisation
