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---
title: Lecture Notes - AIS2204 Maskinsyn
categories: Module
---
# Chapter 1-2. Introduction and 3D Modelling (two weeks)
**Note** The lecture plan as posted at the start of semester is last
year's schedule. There will not be any revolutionary changes, but
it will be reviewed and amended as we go along.
**Dates** 25-26 August + 1-2 September
# Chapter 1-3. Introduction; 3D geometry and projections
**Dates** 21, 24, 28, and 31 August, and 4 and 7 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 |
| 4 | [Image Formation]() | Ma 2004:Ch 3-3.3.1 (SZ 6) | (self-study) projection, lens/camera | |
| 5 | [Camera Calibration]() | Ma 2004:Ch 3.3-3.3.3 | (self-study) Calibration, Radial Distortion etc. |
| 6 | [Three-week Recap]() | Ma 2004:Ch 1-3 | 3D Motion and 2D Projections | To be adapted to class need|
| 7 | Recap: [Camera Calibration]() | Ma 2004:Ch 3 | | Many students needed more time to get the calibration to work. |
**Dates** 8-9 and 15 September
**Note** It could have been useful to do a session on Theorem 2.8, training proof
reading skills.
# Chapter 4. Feature Tracking (three weeks)
# Chapter 4. Feature Tracking
(Last year's session numbers in parentheses.)
We need to interleave this with material from later blocks to have
project tracker run over midterm.
[Study Technique]() may be a good candidate.
| # | 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 ?)
| -: | :- | :- | :- | :- |
| 8 (14 Sep) | [Image Filters]() | Convolution. Filters. Blurring. | |
| 9 | [Corner Detection]() | Ma 2004:Ch 4.3, 4.A (SZ 4) | Calculate Gradient. Harris Feature Detector. | |
| 10 (21 Sep) | [Tracking Features]() | Ma 2004:Ch 4-4.2 | Tracking of Features. Tracking of Edges. | |
| 11 | [SIFT]() | | Feature Matching. Feature Descriptor. | |
| 12 (28 Sep) | [Edges]() | Ma 2004:Ch 4.4 | Canny, connected components, line fitting | |
| 13 | [Colour]() Models |
| 14 (5 Oct) | [Relative Pose]() | Ma 2004:Ch 5.1 | Triangulation. Relative Pose. Essential Matrix. |
| 15 | [Project Tracker]() | | [Multiscale Detection]() | |
| 16 (12 Oct) | *Self-Study* | Continue with [Tracking Features]() | - | - |
| 17 | Recap | | Status, review, repetition | |
| # | 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 | |
# Chapter 5. Projective Reconstruction
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
| # | Topic | Reading | Keywords | Status |
|---|---------------|-------------------|-----------------------------| -: |
| 18 (19 Oct) | [Eight-point algorithm]() | Ma 2004:Ch 5.2 | Calculate Essential Matrix | OK |
| 19 | [Study Technique]() | Ma 2004:Ch 5.1 | Proof reading. | OK |
| 20 (26 Oct) | [Planar Scenes]()| Ma 2004:Ch 5.3 | |
| 21 (30 Oct) | [Epipolar Geometry]()| Ma 2004:Ch 5.1-3 | |
| 22 | self study [3D Reconstruction]() | Ma 2004:Ch 5.1-2 | Continuous on 18 [Eight-point algorithm]() using real image data | OK |
# New. Machine Learning
# Chapter X. Machine Learning
| # | Topic | Reading | Keywords |
|----|---------------|-------------------|-----------------------------|
| 23-24 | [Neural Networks]() | Szeliski 2022 Chapter 5 | Training. Testing |
| 25 | [Statistics]() | | Evaluation, Standard Deviation |
| 26 |
| 26 | [Object Detection]() | Szeliski 2022 Chapter 6 (6.3 in particular) | Object Detection |
| 27 | [Regression]() | | |
| 28 | [Distorted Space]() + Recap | Ma 2004:Ch 6.1-2 | Questions; Answers; module evalutaiton |
+ 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)
+ The material is under constant review.
+ Any feedback is welcome.
+ Existing notes for [Review]()
# 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
