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