# Overview

## Changes from 9347c71d550d2b72428c203749f8be7a7db26158 to c2d71fa0b28438e261e5bb4a74a7b6d1be7790d2

```---
title: Lecture Notes - AIS2204 Maskinsyn
categories: Module
---

**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.

# 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 | [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.  |

**Note** It could have been useful to do a session on Theorem 2.8, training proof

# Chapter 4. Feature Tracking

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 (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]() |
| 13 | [Colour]() Models |
| 14 (5 Oct) |
| 15  | [Project Tracker]() | | [Multiscale Detection]() | |
| 16 (12 Oct) | *Self-Study* | Continue with [Tracking Features]() | - | - |
| 17 | Recap      | | Status, review, repetition | |

# Chapter 5. Projective Reconstruction

| # | Topic  | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 7/2022 | [Pre- and co-image]() | Ma 2004:Ch 3.3-3.4 | Radial Distortion, Tangential Distortion | |
| 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.  [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 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.  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

# Chapter X.  Machine Learning

| #  | Topic         | Reading | Keywords |
|----|---------------|-------------------|-----------------------------|
| 23-24 | [Neural Networks]() | Szeliski 2022 Chapter 5 | Training. Testing |
| 25 | [Statistics]() |  | Evaluation, Standard Deviation |
| 26 | [Regression]() | | |

+ We should go further into [Object Recognition]() next year

# Last Week.  Summary and Miscellanea

| # | Topic  | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 27 (23) | [Distorted Space]()  | Ma 2004:Ch 6.1-2 |  |
| 28 | Recap  | The Entire Syllabus | Questions & Answers  |

# 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
```