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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

# 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.
  2. Ma 2004 Chapter 11.4.
  3. Ma 2004 Chapter 11.5. Keywords texture, visualisation