Computer Vision Methods Labs

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.

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:

1. 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.
2. Working on the current task. Students are free to ask any specific questions, discuss their current results, resolve any programming issues.
3. Short introduction of the next task. The teacher will briefly introduce the next problem, give some hints and answer possible questions.

You are obliged to carry out all programming tasks at least a minimal required quality. All tasks must be carried out individually! 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.

Your participation on the labs is mandatory; you can be absent at most 3 times.

The grading points from the labs will contribute to 50 percent of your course evaluation. It is required to obtain at least half of the points for each programming task.

Each task uploaded after the due date is penalized as follows:
From 5th task on: 0.015pts for every hour after the deadline (= 2.5pts/7days)
Until 4th task: Upload late by 6 - 24 hours ⇒ -10% of the points, 24 - 48 hours ⇒ -25% of the points and finally 48 - 72 hours ⇒ 40% of the maximum points. Tasks not delivered until 72 hours after the due date (i.e. three days after the due date) without a relevant excuse will not be accepted and automatically lead to failing the course.(this rule is no more valid)

Computer vision: Facts & Fiction series
Computer Vision course at CMU

Course Assistants
Daniel Barath	Dmytro Mishkin	Filip Naiser	Ondřej Drbohlav (lead)