Course development
On this page, you can track the course's development over time. Once the course analysis is published, data for each course offering is displayed, including the number of registered students, course results, and planned improvements for the next session. All course syllabuses and course memos are shown on the page Archive.
The information can help prospective, current, and former students with course selection, or to follow up on their own participation. Teachers, course coordinators, examiners, program directors, and others can use the page as a resource for course development.
2025
Course analysis not published
2024
Course analysis not published
2023
Doktorand ( Start date 30 Oct 2023, English ) , CVHT23 ( Start date 30 Oct 2023, English )
Changes planned for the next course offering
No information inserted
Coordinator
Mårten BjörkmanExaminers
Mårten BjörkmanStudents
123*Course syllabus
Course syllabus DD2423 ( Autumn 2021 - Spring 2024 )Course memo
No course memo addedNo course memo addedCompulsory within programme
TSCRMCourse analysis
Course analysis DD2423: 21 Aug 2024Changes introduced for this course offering
Given that the course went through a major revision last year, few changes were made for this course round. Incremental improvements were made with topics presented in a slightly different order to make it more logical and more recent computer vision methods were added. Another change made last year, which was repeated this course round, was the introduction of a grade E quiz. The only difference was that the set of available quiz questions was extended, with earlier ambiguous questions updated or removed.Course data has been registered manually
Examination grade
82.9%*
2022
CVHT22 ( Start date 31 Oct 2022, English )
Changes planned for the next course offering
No information inserted
Coordinator
Mårten BjörkmanExaminers
Mårten BjörkmanStudents
158Course syllabus
Course syllabus DD2423 ( Autumn 2021 - Spring 2024 )Course memo
No course memo addedCompulsory within programme
TSCRMCourse analysis
Course analysis DD2423: 12 Oct 2023Changes introduced for this course offering
A full revision of the lectures was made this year. Every slide was updated, with some material being either discarded or replaced. Material related to image enhancement and segmentation was considerably shortened and replaced by two new lectures, an introduction to deep learning and a new lecture on novel view synthesis. The goal was to focus on traditional methods of computer vision that are just as relevant today, as they were before deep learning was introduced, as well as on topics today dominated by deep learning. Another significant change in the course was the introduction of an online quiz for grade E in Zoom. There were two motivations for such a change, to promote continuous learning throughout the course and encourage students to complete the voluntary quizzes during the course and to relieve students from some of the burden they experience when preparing for the exam by allowing them to focus on more problem-related questions.Course data has been registered manually
Examination grade
83.5%*
2021
CVHT21 ( Start date 01/11/2021, English )
Changes planned for the next course offering
No information inserted
Coordinator
Mårten BjörkmanExaminers
Mårten BjörkmanStudents
143Course syllabus
Course syllabus DD2423 ( Autumn 2021 - Spring 2024 )Course memo
No course memo addedCompulsory within programme
TMLEM, TSCRMCourse analysis
Course analysis DD2423: 12 Sept 2022Changes introduced for this course offering
Due to the pandemic, the course was run in a hybrid format with lectures on campus but also broadcast in Zoom. Lab presentations and help sessions were however held fully online, but this year there was an option to do the labs in Python instead of Matlab. Q&A sessions were introduced, during which students could ask any question related to the course. The course content was updated for about half the lectures with more examples of deep learning-based methods for computer vision in particular.Examination grade
66.4%
2020
Autumn 2020-1 ( Start date 26/10/2020, English )
Changes planned for the next course offering
No information inserted
Coordinator
Mårten BjörkmanExaminers
Mårten BjörkmanStudents
134Course syllabus
Course syllabus DD2423 ( Autumn 2019 - Spring 2021 )Course memo
No course memo addedCompulsory within programme
TMLEM, TSCRMCourse analysis
Course analysis DD2423: 9 Sept 2022Changes introduced for this course offering
Due to the pandemic, the course this year had to be changed into a fully online format, where only the exam was on campus. Students that could not physically attend the exam were given the opportunity to attend the re-exam that was also held online. Lectures were given in Zoom and recorded with videos published on Canvas. Lab presentations and help sessions were also held online. However, while presentation sessions were densely scheduled during the last days of each lab week, help sessions were more sparsely spread over the whole period giving students and TAs more flexibility.Examination grade
60.4%
2019
bildat19 ( Start date 28/10/2019, English )
Changes planned for the next course offering
No information inserted
Coordinator
Mårten BjörkmanExaminers
Mårten BjörkmanStudents
215Course syllabus
Course syllabus DD2423 ( Autumn 2019 - Spring 2021 )Course memo
No course memo addedCompulsory within programme
CMETE, TSCRMCourse analysis
Course analysis DD2423: 8 Sept 2022Changes introduced for this course offering
A change compared to earlier course rounds introduced this year, was to more actively use Canvas to provide direct feedback and help, in particular for questions related to the labs. TAs were staffed to moderate discussions related to different aspects of the course and provide quick feedback to students in need of help. This encouraged students to directly ask for help, rather than wait until the dedicated lab help sessions. Another change was that the re-exam was held online due to the COVID pandemic. With questions posted online in Canvas students worked from home while being connected through Zoom, but without the necessity of having the camera turned on. Canvas was also used to check IDs, while the chat in Zoom could be used for questions.Examination grade
71.2%
2018
bildat18 ( Start date 29/10/2018, English )
Changes planned for the next course offering
No information inserted
Coordinator
Mårten BjörkmanExaminers
Mårten BjörkmanStudents
223Course syllabus
Course syllabus DD2423 ( Autumn 2015 - Autumn 2018 )Course memo
No course memo addedCompulsory within programme
CMETE, TSCRMCourse analysis
Course analysis DD2423: 8 Sept 2022Changes introduced for this course offering
The course was updated with more examples of deep learning being used in computer vision, in order to better align with courses in machine learning. A short introduction to typical neural networks in computer vision was given in the first lecture, which is followed by examples in various lectures about feature matching, segmentation, object recognition and motion analysis. A set of voluntary quizzes were also introduced after each lecture. The quizzes serve two purposes. First, it gives students an idea of how much in depth they need to go into each individual topic. Since the course is introductory it spans the whole field of computer vision and the amount of available literature is vast. A second reason is to focus on important concepts that are often misunderstood. Quiz questions are often phrased such that incorrect answers easily lead to cognitive dissonance when an explanation is given at the end. Finally, the quiz gives the lecturer feedback on what should be reiterated during lectures.Examination grade
61%
2017
bildat17 ( Start date 30/10/2017, English )
Changes planned for the next course offering
No information inserted
Coordinator
Mårten BjörkmanExaminers
Mårten BjörkmanStudents
182Course syllabus
Course syllabus DD2423 ( Autumn 2015 - Autumn 2018 )Course memo
No course memo addedCompulsory within programme
TSCRM, CMETECourse analysis
Course analysis DD2423: 8 Sept 2022Changes introduced for this course offering
Besides some updates of the course material itself, a few important changes were made to the lab course to reduce the burden on teaching assistants and make students focus more on the essentials: 1) Students were asked to upload to Canvas summaries of lab results with reflections on key concepts from the labs. 2) Students presented their labs during 20 minute individual sessions, instead of 30 minute sessions that were used before. This was possible by focusing on key concepts from the labs, rather than on the results, results that had already covered in the uploaded reports. 3) Instead of using computer halls, a regular seminar hall was used. Rather then showing results on-screen, the uploaded reports were used as support for the students in their presentations. The goal of these changes were to force students to reflect more on key concepts and less on the code and results.Examination grade
58.8%