183.585 Computer Vision
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2022W, VU, 3.0h, 4.5EC
Lecture TubeTUWEL

Course evaluation

Properties

  • Semester hours: 3.0
  • Credits: 4.5
  • Type: VU Lecture and Exercise
  • LectureTube course
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to identify, explain and contrast the most important theories, principles, concepts and algorithms of Computer Vision. Their knowledge and understanding correspond to the state of the art literature in the field of computer vision. They are able to apply appropriate formal-mathematical methods for modeling, abstraction, solution finding and evaluation, and gain problem-formulation and problem-solving skills.

Subject of course

How can computers understand the visual world of humans?


This course treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. Topics include: Structure of Images, Texture, Scenes, and Context, Feature Based Alignment, Stereo Correspondence, Structure from Motion, Image Stitching, Computational Photography, Image Based Rendering, and Baysian Modeling for Object Recognition. This course is designed for students interested in vision, artificial intelligence, or machine learning. The course offers a broad introduction to the field, the current problems and theories, the basic mathematics, and some interesting algorithms and the possibility to apply the contents learned direct in exercises.

Teaching methods

Frontal presentation and written exam, independent solving and scientific discussion of subject-related examples and submissions. The course consists of a lecture part and an exercise part. The contents and concepts described are explained in the context of the lecture unit and practically tested and applied in the practice section.

Exercise part: Six examples (e.g., Image Stitching, Image classification with Deep Learning) in Python in groups of three people each. Two oral interviews regarding the submitted code.

Mode of examination

Written

Additional information

Introduction to the exercise part on 03.10.2022 - 15:00 c.t.

First lecture on October 10!


Please check the schedule on the course homepage below, lectures will not take place on every reserved date as shown in TISS.

Fur further information check the homepage of the lecture.

ECTS Breakdown: 4.5 ECTS = 112.5h

24h    Lecture
12h    Development of Python code for solving the practical assignments (with tutor support)
25h    Preparation of report of the practical assigments
0.5h   Exercise interview
49h Exam Preparation
2h   Exam
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112.5h

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Mon15:00 - 17:0003.10.2022 - 23.01.2023FAV Hörsaal 1 - INF Lecture
Mon17:00 - 19:0003.10.2022 - 23.01.2023FAV Hörsaal 1 - INF Lecture
Tue17:00 - 19:0004.10.2022 - 24.01.2023FAV Hörsaal 1 - INF Vorlesung
Computer Vision - Single appointments
DayDateTimeLocationDescription
Mon03.10.202215:00 - 17:00FAV Hörsaal 1 - INF Lecture
Mon03.10.202217:00 - 19:00FAV Hörsaal 1 - INF Lecture
Tue04.10.202217:00 - 19:00FAV Hörsaal 1 - INF Vorlesung
Mon10.10.202215:00 - 17:00FAV Hörsaal 1 - INF Lecture
Mon10.10.202217:00 - 19:00FAV Hörsaal 1 - INF Lecture
Tue11.10.202217:00 - 19:00FAV Hörsaal 1 - INF Vorlesung
Mon17.10.202215:00 - 17:00FAV Hörsaal 1 - INF Lecture
Mon17.10.202217:00 - 19:00FAV Hörsaal 1 - INF Lecture
Tue18.10.202217:00 - 19:00FAV Hörsaal 1 - INF Vorlesung
Mon24.10.202215:00 - 17:00FAV Hörsaal 1 - INF Lecture
Mon24.10.202217:00 - 19:00FAV Hörsaal 1 - INF Lecture
Tue25.10.202217:00 - 19:00FAV Hörsaal 1 - INF Vorlesung
Mon31.10.202215:00 - 17:00FAV Hörsaal 1 - INF Lecture
Mon31.10.202217:00 - 19:00FAV Hörsaal 1 - INF Lecture
Mon07.11.202215:00 - 17:00FAV Hörsaal 1 - INF Lecture
Mon07.11.202217:00 - 19:00FAV Hörsaal 1 - INF Lecture
Tue08.11.202217:00 - 19:00FAV Hörsaal 1 - INF Vorlesung
Mon14.11.202215:00 - 17:00FAV Hörsaal 1 - INF Lecture
Mon14.11.202217:00 - 19:00FAV Hörsaal 1 - INF Lecture
Mon21.11.202215:00 - 17:00FAV Hörsaal 1 - INF Lecture

Examination modalities

Preparation of practical examples/submission discussion, written exam. (weighted 40:60).

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Tue16:00 - 18:0007.03.2023FAV Hörsaal 1 - INF written13.02.2023 09:00 - 06.03.2023 09:00TISSComputer Vision Examination
Wed15:00 - 17:0010.05.2023FAV Hörsaal 1 - INF written19.04.2023 09:00 - 08.05.2023 09:00TISSComputer Vision Examination
Wed15:00 - 17:0007.06.2023FAV Hörsaal 1 - INF written24.05.2023 09:00 - 05.06.2023 09:00TISSComputer Vision Examination

Course registration

Begin End Deregistration end
12.09.2022 09:00 06.10.2022 09:00 06.10.2022 09:00

Registration modalities

Student groups are formed by registering for one of the groups listed in TISS. Please register for a group at your preferred time of day: Thursday 9-11 or 11-13. In the first session  the group assignments will be fixed.

Curricula

Literature

 The course is based on a new book,Computer Vision: Algorithms and Applications, by Richard Szeliski, which is on the web for free right now.

Previous knowledge

  • Algebra and Discrete Mathematics
  • Algorithms and Data Structures
  • Analysis
  • program design
  • Foundations of Visual Computing

of the currently valid curriculum of computer science bachelor's degree media and visual computing.

Python knowledge is an advantage, but not required.

Miscellaneous

Language

if required in English