# Overview

## Changes from d457afe455752a350b06849d8605332cc7e927c2 to current

```---
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)
| 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.  |

| # | 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 |
**Note** It could have been useful to do a session on Theorem 2.8, training proof
reading skills.

**Dates** 8-9 and 15 September
# Chapter 4. Feature Tracking

# Chapter 4. Feature Tracking (three weeks)
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. |
| 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) | [Edges]() | Ma 2004:Ch 4.4 | Recap.  Complete Project| TODO |
| -: | :- |  :- |  :- |  :- |
| 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 | |

+ 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

# Chapter 5.  Projective Reconstruction (two weeks ?)
| # | 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 |

This plan is tentative and may be shortened as well as rearranged.
If necessary, Session 16 will be a recap of past material, particularly
the project, and the rest of the program shifted one session.
We will not know this until Session 15.
# Chapter X.  Machine Learning

| # | Topic  | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 16 | [Relative Pose]() | Ma 2004:Ch 5.1 | Triangulation. Relative Pose. Essential Matrix. |
| 17 | [Eight-point algorithm]() | Ma 2004:Ch 5.2 | Calculate Essential Matrix |
| 18 | [Study Technique]() | Ma 2004:Ch 5.1 | Proof reading. |
| 19 | [Synthetic Experiment]()  | Ma 2004:Ch 5.1-2 |  |
| 20 | [Planar Scenes]()| Ma 2004:Ch 5.3 | |
| 22 | [Epipolar Geometry]()| Ma 2004:Ch 5.1-3 | |
| #  | Topic         | Reading | Keywords |
|----|---------------|-------------------|-----------------------------|
| 23-24 | [Neural Networks]() | Szeliski 2022 Chapter 5 | Training. Testing |
| 25 | [Statistics]() |  | Evaluation, Standard Deviation |
| 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 |

*Notes from last 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

TBC

# Chapter 6 and 11.  Projective Reconstruction (many weeks)

This will be shortened and probably based on different sources.

| # | 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 from last 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]()?
# Other Material.

+ [Overview of Python Demoes](Python/Overview)
+ The material is under constant review.
+ Any feedback is welcome.
+ Existing notes for [Review]()

# 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

```