Table of Contents
Computer Vision Methods Labs
Labs plan
Labs are organized in four main topics: Correspondence problem, Indexing and image retrieval, Object tracking, Convolutional neural networks. Each topic is covered by approximately two to four labs.
| Week | Date | Topic | Teacher | Recording |
|---|---|---|---|---|
| 1 | 18.2. | Introduction to Image Processing in python using PyTorch. | DM | recording 2022 |
| 2 | 25.2. | Debugging pytorch code. | DM | |
| 3 | 4.3. | Correspondence problem I, detection of the interest points. | DM | recording 2022 |
| 4 | 11.3. | Correspondence problem II, computing local invariant description. | DM | recording 2022 |
| 5 | 18.3. | Correspondence problem III, finding tenative correspondences and RANSAC. | DM | recording 2022 |
| 6 | 25.3. | Correspondence problem, summary. | DM | |
| 7 | 1.4. | Convolutional Neural Networks: training a classifier | LN | recording 2022 |
| 8 | 8.4. | Convolutional Neural Networks II: debugging training process | LN | |
| 9 | 15.4. | Image Retrieval, BoW TF-IDF, fast spatial verification. | PS, GKZ | |
| 10 | 22.4. | Assignment defence | ||
| 11 | 29.4. | Deep metric learning | PS, GKZ | |
| 12 | 6.5. | Self-supervised Learning | PS, GKZ | |
| 13 | 13.5. | Rector's sport day (no teaching at the CTU) | ||
| 14 | 20.5. | Tracking I, Kanade-Lucas-Tomasi tracking (KLT tracker) | JS |
Labs will be accompanied with a simple programming task. Detailed specification of the tasks is described in each of the labs. Students will upload their results and their codes through the BRUTE system.
Each lab will usually consists of three parts:
- Discussion on the last lecture. Students will be free to ask any questions related to the last lecture. At the end of this session, a teacher will pose a question to a volunteer/random student. If the student answers correctly, he/she will get a single bonus point.
- Working on the current task. Students are free to ask any specific questions, discuss their current results, resolve any programming issues.
- Short introduction of the next task. The teacher will briefly introduce the next problem, give some hints and answer possible questions.
Assessment
You are obliged to carry out all programming tasks at least a minimal required quality. All tasks must be carried out individually, with a possible help of AI. You are free to discuss the problems with your colleagues, however the code must be written strictly by yourself. See plagiarism if you are unsure what is allowed.
AI usage policy
You are free to use tools like ChatGPT to help you with the assignment. During the labs, you would be required to answer questions about the code and explain how it works, how it could be modified, what you are proud of, what you have struggled with, etc.
Such defence of the assignment will be done several times per semester and will give you a coefficient from 0 to 1.2, which then is multiplied by score in BRUTE. This way one could have perfect assignment in BRUTE, but if he or she is not able to explain how it works, the final score for the lab will be zero.
Evaluation Policy
The points from the labs will contribute to 50 percent of your course evaluation.
There will be 11 tasks awarded with points throughout the semester (a new task will be given out every week in the lab, with the exception of two labs
which are intended to help students with debugging). On top of these tasks, there will also be ability to get bonus points in some labs.
Each task has a deadline of 2 weeks and 1 day.
This is mostly relevant to the defence of the lab (see the AI policy above)
Useful Links
Questions
If you have any question, use our discussion forum preferably. For organizational questions, contact Jan Čech, who is responsible for the MPV labs. For technical questions, please contact the relevant teaching assistant.
| Course Assistants | |||||
 |  |  |  |  |  |
| Lukáš Neumann | Dmytro Mishkin | Pavel Šuma | Jonáš Šerých | Giorgos Kordopatis-Zilos | Jan Čech (lead) |