Revision a101d28ae6e46387f468b2f5822da19e19b9784f (click the page title to view the current version)
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)
# | 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. | |
11 (new) | Project Tracker | new Lecture Multiscale Detection | OK | |
12 (16) | SIFT | NB Feature Matching. Feature Descriptor. | OK | |
13-14 | Self-Study | Continue with Tracking Features | ||
15 (11) | Edges | Ma 2004:Ch 4.4 | Recap. Complete Project |
- 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 ?)
This plan is tentative and may be shortened as well as rearranged. If necessary, Session 16 will be a recap of past material, particularly the project, and the rest of the program shifted one session. We will not know this until Session 15.
# | Topic | Reading | Keywords |
---|---|---|---|
16 | Relative Pose | Ma 2004:Ch 5.1 | Triangulation. Relative Pose. Essential Matrix. |
17 | Eight-point algorithm | Ma 2004:Ch 5.2 | Calculate Essential Matrix |
18 | Study Technique | Ma 2004:Ch 5.1 | Proof reading. |
19 | Synthetic Experiment | Ma 2004:Ch 5.1-2 | |
20 | Planar Scenes | Ma 2004:Ch 5.3 | |
22 | Epipolar Geometry | Ma 2004:Ch 5.1-3 |
Notes from last year
- Synthetic Experiment should be merged into Eight-point algorithm
- Study Technique should probably be done earlier in the semester
- 3D modelling took a lot of time. Many did not realise that they needed to find triangles. Rectangles mixed with triangles caused problems.
- Generally, the preliminary steps of the exploratory exercises should have been premade, to save time for the students.
- Algorithm implementation is difficult and require sample solutions
New. Machine Learning
TBC
Chapter 6 and 11. Projective Reconstruction (many weeks)
This will be shortened and probably based on different sources.
# | 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
- We can skip Stratified Reconstruction and Partial Scene Information
- Distorted Space may be interesting as an introduction to inner product spaces.
- 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 |
- Ma 2004 Chapter 10. Partial Scene Knowledge
- This is referenced as a building block in Chapter 11.
- Ma 2004 Chapter 11.4.
- Ma 2004 Chapter 11.5. Keywords texture, visualisation