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Overview

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

# Chapter 1-2. Introduction and 3D Modelling (two weeks)

**Dates** 25-26 August + 1-2 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 |

**Dates** 8-9 and 15 September

# Chapter 4. Feature Tracking (three weeks)

(Last year's session numbers in parentheses.)

| # | 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 ?)

| # | 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.  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

# New.  Machine Learning

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

+ 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

# Chapter 6.1-2.  Distorted space


| # | Topic  | Reading | Keywords |
|---|---------------|-------------------|-----------------------------|
| 23/2021 | [Distorted Space]()  | Ma 2004:Ch 6.1-2 |  |
| 26/2021 | [Real World Reconstruction]() | Ma 2004:Ch 11 |  |

*Notes from last year*

1. What can we make out of [Real World Reconstruction]()?


# Closure/Tentative

| # | 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  | |

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