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Lecture Notes - AIS2204 Maskinsyn

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.
  2. 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.
  3. Note from 2021: Generally, the preliminary steps of the exploratory exercises should have been premade, to save time for the students.
  4. Note from 2021: Algorithm implementation is difficult and require sample solutions

New. Machine Learning

TBC

  • 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 and 11. Projective Reconstruction (many weeks)

This section is likely to go away, or drastically shortened. Session 26 is the most relevant one.

# 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?

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
  1. Ma 2004 Chapter 10. Partial Scene Knowledge
    • This is referenced as a building block in Chapter 11.
  2. Ma 2004 Chapter 11.4.
  3. Ma 2004 Chapter 11.5. Keywords texture, visualisation