# Overview

## Changes from b3b64bbe3eb49db44604d02cdb57029521e9ab99 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.

# Chapter 1-3. Introduction; 3D geometry and projections

| # | Session Notes | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 1 | [Introduction]() | Ma 2004:Ch 1 (Ch 2.1 and 2.3)  | Practical matters. Software installation. Reap 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, 3D Transformations in Python |
| 4 | [3D Modelling Part II]() | | Velocity transformations.  Recap.  Questions. |
**Dates** 21, 24, 28, and 31 August, and 4 and 7 September

# Chapter 3. Image Formation (two weeks)
| # | 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.  |

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

# Chapter 4. Feature Tracking (three weeks)
# Chapter 4. Feature Tracking

| # | Topic  | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| new | [Signal Processing]() | | Convolution.  Filters.  Blurring. |
| 8 | [Corner Detection]() | Ma 2004:Ch 4.3, 4.A (SZ 4) | Calculate Gradient.  Harris Feature Detector. |
| 9 | [Corner Detection]() continued | | |
| 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]() | | |
| 15 | [Tracking Features 2]() | |
| 16 | [SIFT]() | | Feature Matching.  Feature Descriptor. |
We need to interleave this with material from later blocks to have
project tracker run over midterm.
[Study Technique]() may be a good candidate.

*Note* Session 12 was planned Self-Study due to staff seminar.
Session 13-14 became self-study because of sick leave, and negligible
| # | 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 |
| 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 | |

*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

# Chapter 5-6 and 11.  Projective Reconstruction (many 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.
# Chapter X.  Machine Learning

| # | 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 | |
| 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 |  |
| #  | 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 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 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
# Other Material.

# Closure/Tentative
+ [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 |
|---|---------------|-------------------|-----------------------------|
| 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

```