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Overview

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---
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 1-2. Introduction and 3D Modelling 

**Dates** 21, 24, 28 August

| # | 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 |

# Chapter 3. Image Formation 

**Dates** 31 August, 4 and 7 September

| # | Topic  | Reading | Keywords | Status |
|---|---------------|-------------------|-----------------------------|------|
| 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 | |

# Chapter 2-3. Review

| # | Topic  | Reading | Keywords | Status |
|---|---------------|-------------------|-----------------------------|------|
| 4 | [3D Modelling Part II]() |   | Velocity transformations.  Recap.  Questions. |  To be adapted to class |

# Chapter 4. Feature Tracking 

(Last year's session numbers in parentheses.)

| # | Topic  | Reading | Keywords | Status |
|---|---------------|-------------------|-----------------------------|----|
| 8 (new) | [Image Filters]() | | Convolution.  Filters.  Blurring. | |
| 9 (8-9) | [Corner Detection]() | Ma 2004:Ch 4.3, 4.A (SZ 4) | Calculate Gradient.  Harris Feature Detector. | |
| 8 | | Convolution.  Filters.  Blurring. | |
| 9 | [Corner Detection]() | Ma 2004:Ch 4.3, 4.A (SZ 4) | Calculate Gradient.  Harris Feature Detector. | |
| 10 | [Tracking Features]() |  Ma 2004:Ch 4-4.2 | Tracking of Features. Tracking of Edges. | |
| 11 (new)  | [Project Tracker]() | | [Multiscale Detection]() | |
| 12 (16) | [SIFT]() | | Feature Matching.  Feature Descriptor. | |
| 11  | [Project Tracker]() | | [Multiscale Detection]() | |
| 12 | [SIFT]() | | Feature Matching.  Feature Descriptor. | |
| 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 | |
| 15 | Recap      | | Status, review, repetition | |
| 16 | [Edges]() | Ma 2004:Ch 4.4 | Canny, connected components, line fitting | |

# Chapter 5.  Projective Reconstruction 

| # | 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 | |

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 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.  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