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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.
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
- 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
# 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
# Chapter 6.1-2. Distorted space
# Last Week. Summary and Miscellanea
| # | Topic | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 23/2021 | [Distorted Space]() | Ma 2004:Ch 6.1-2 | |
| 26/2021 | [Real World Reconstruction]() | Ma 2004:Ch 11 | |
*Notes from last year*
1. What can we make out of [Real World Reconstruction]()?
# Other Material.
+ [Overview of Python Demoes](Python/Overview)
# Closure/Tentative
# Old Material.
| # | Topic | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 27 | [Continuous Motion]() | Ma 2004:Ch 5.4 | |
| 28 | Tentative Seminar: Applications | TBA | **TBC** - we may decide to move on to feature tracking |
| 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
