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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 |
|---|---------------|-------------------|-----------------------------|
| 1 | [Introduction]() | Ma 2004:Ch 1 (Ch 2.1 and 2.3) | Practical matters. Software installation. Recap of linear algebra. |
| 2 | [3D Modelling]() | Ma 2004:Ch 2, App A (SZ 2) | 3D modelling, motion |
| 3 | [3D Objects in Python]() | Tutorials | Homogeneous co-ordinates. General Rotations. 3D Transformations in Python |
| 4 | [3D Modelling Part II]() | | Velocity transformations. Recap. Questions. |
# Chapter 3. Image Formation (two weeks)
| # | Topic | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 5 | [Image Formation]() | Ma 2004:Ch 3-3.3.1 (SZ 6) | projection, lens/camera |
| 6 | [Camera Calibration]() | Ma 2004:Ch 3.3-3.3.3 | Calibration, Radial Distortion etc. |
| 7 | [More Camera Mathematics]() | Ma 2004:Ch 3.3-3.4 | Radial Distortion, Tangential Distortion |
**Dates** 8-9 and 15 September
# Chapter 4. Feature Tracking (three weeks)
**Dates** 8-9 and 15 September
| # | Topic | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| new | [Image Filters]() | | Convolution. Filters. Blurring. |
| 8 | [Corner Detection]() | Ma 2004:Ch 4.3, 4.A (SZ 4) | Calculate Gradient. Harris Feature Detector. |
| 9 | [Corner Detection]() continued | | **new** Multi-Scale Detection |
| 10 | [Tracking Features]() | Ma 2004:Ch 4-4.2 | Tracking of Features. Tracking of Edges. |
| 11 | [Edges]() | Ma 2004:Ch 4.4 | |
| 12 | *Self-Study* | Continue with [Tracking Features]() |
| 13 | *Standup* | Continue with [Tracking Features]() |
| 14 | [SIFT]() | | **NB** |
| 15 | [Tracking Features 2]() | **TODO** Long project? |
| 16 | [SIFT]() | | Feature Matching. Feature Descriptor. |
| 8 (new) | [Image Filters]() | | Convolution. Filters. Blurring. |
| 9 (8) | [Corner Detection]() | Ma 2004:Ch 4.3, 4.A (SZ 4) | Calculate Gradient. Harris Feature Detector. |
| 10 (9) | [Corner Detection]() continued | | **new** Multi-Scale Detection |
| 11 (10) | [Tracking Features]() | Ma 2004:Ch 4-4.2 | Tracking of Features. Tracking of Edges. |
| 12 (11) | [Edges]() | Ma 2004:Ch 4.4 | |
| 13-14 | *Self-Study* | Continue with [Tracking Features]() |
| 15 | [Tracking Features 2]() | Recap. Long project? |
| 16 | [SIFT]() | | **NB** Feature Matching. Feature Descriptor. |
+ 8 - 16 September
+ 9-12 - 22-23 and 29-30 September
+ 13-14 - 6-7 October - staff seminar - possibly self-managed work only
+ 20-21 - 13-14 October - midterm - regular teaching
*Note* Session 12 was planned Self-Study due to staff seminar.
Session 13-14 became self-study because of sick leave, and negligible
progress was made this week.
*Note for next year*
We should have had one session on blurring filters and similar
techniques from image processing before starting on Corner Detection
(Session 8).
This would have made the introduction of signal differentiation smoother.
# Chapter 5. Projective Reconstruction (two weeks ?)
This plan is tentative.
| # | 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 | [Synthetic Experiment]() | Ma 2004:Ch 5.1-2 | |
| 21 | [Planar Scenes]()| Ma 2004:Ch 5.3 | |
| 22 | [Epipolar Geometry]()| Ma 2004:Ch 5.1-3 | |
*Notes for next year*
1. [Synthetic Experiment]() should be merged into
[Eight-point algorithm]()
2. [Study Technique]() should probably be done earlier in the semester
3. 3D modelling took a lot of time. Many did not realise that they
needed to find triangles.
Rectangles mixed with triangles caused problems.
4. Generally, the preliminary steps of the exploratory exercises should have been
premade, to save time for the students.
5. Algorithm implementation is difficult and require sample solutions
# New. Machine Learning
# Chapter 6 and 11. Projective Reconstruction (many weeks)
| # | Topic | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 23 | [Distorted Space]() | Ma 2004:Ch 6.1-2 | |
| 24 | [Stratified Reconstruction]() | Ma 2005:Ch 6.3-4 | |
| 25 | [Partial Scene Information]() | Ma 2005:Ch 6.5 | |
| 26 | [Real World Reconstruction]() | Ma 2004:Ch 11 | |
*Notes for next year*
1. We can skip [Stratified Reconstruction]()
and [Partial Scene Information]()
2. [Distorted Space]() may be interesting as an introduction to
inner product spaces.
3. What can we make out of [Real World Reconstruction]()?
# Closure/Tentative
| # | 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 |
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
