700.304 (23W) Fundamentals of Image Processing
Überblick
- Lehrende/r
- LV-Titel englisch Fundamentals of Image Processing
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Blended-Learning-Lehrveranstaltung
- Online-Anteil 24%
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 15 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 18.10.2023
- eLearning zum Moodle-Kurs
- Seniorstudium Liberale Ja
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Welcome to the introductory course on the fundamentals of digital image processing. This course will lay the groundwork for understanding how to effectively process images and extract valuable information from them.
Throughout the course, you will gain a solid understanding of the basic concepts and techniques used in image processing. You will learn how to apply various methods to enhance, manipulate, and analyze images, enabling you to extract the desired information effectively. By the end of the course, you will have achieved the following learning outcomes:
Proficiency in applying fundamental image processing methods: You will be equipped with the necessary skills to perform essential image processing tasks, such as filtering, segmentation, and enhancement. You will gain hands-on experience in implementing these techniques using industry-standard tools.
Understanding of image features and object recognition: You will develop a deep understanding of image features, including edges, textures, and shapes, and learn how to utilize them for object recognition and classification. You will explore advanced algorithms and methodologies used in this context.
Application of image processing knowledge to real-world problems: The acquired knowledge and skills in image processing will be applied to solve practical problems encountered in various industries and research domains. You will gain the ability to analyze and interpret images, enabling you to address challenges in fields such as medical imaging, surveillance, and remote sensing.
Lehrmethodik inkl. Einsatz von eLearning-Tools
The course will be structured into modules, each focusing on key aspects of image processing, such as filtering, segmentation, enhancement, object recognition, and real-world applications. Teaching will involve interactive lectures using visual aids, real-world examples, and live demonstrations to encourage questions and discussions. Practical exercises and assignments will require students to apply the learned concepts using industry-standard tools, promoting hands-on experience. Students will be organized into small teams for group projects, aimed at solving real-world problems collaboratively, fostering practical application of knowledge, and developing problem-solving skills. A dedicated eLearning platform will provide access to a variety of online resources including video tutorials, reading materials, and software tools, serving as a repository for all course materials and allowing students to reinforce their learning at their convenience. Regular quizzes and assessments will be conducted to evaluate students' understanding and practical application of concepts, with feedback provided to identify areas for improvement and enhance the learning experience.
Inhalt/e
- Understanding the concept of image (e.g., camera technology, digital images, image types and representation, thermal images)
- Simple image manipulations (e.g., color depth, size, cropping)
- Image filtering and convolutions
- Morphological transformation and thresholding
- Geometric transformations
- Feature detection
- Image registration and stitching
- Image restoration (e.g., de-blurring, interpolations, Fourier domain)
Erwartete Vorkenntnisse
For this course, prerequisites typically include basic programming skills, preferably in Python, as many image processing tasks involve coding. A strong mathematical background in linear algebra, calculus, and statistics is crucial for understanding and implementing image processing techniques.
Literatur
- Digital Image Processing (third Edition); Rafael C. Gonzalez, Richard E. Woods.
- Computer Vision: Algorithms and Applications; Richard Szeliski.
Prüfungsinformationen
Prüfungsmethode/n
Format
- An oral exam using a digital platform (e.g., BBB via Moodle).
Scheduling
- Students will be given a specific time slot at least one week before the exam date.
- It is crucial for students to check their technical setup before the exam day to avoid any technical difficulties.
Prüfungsinhalt/e
Preparation Tips
- Review course materials, including lecture notes, readings, and assignments.
- Practice speaking and explaining concepts to peers or in front of a mirror.
- Think critically about the topics and anticipate potential counterarguments or challenges.
Beurteilungskriterien/-maßstäbe
Evaluation Components
- Project: 20%
- Homework: 20%
- Class Activity: 10%
- Online Oral Exam: 50%
Evaluation Criteria
- Depth of Understanding: Demonstrated thorough comprehension of course content
- Ability to connect course concepts to real-world applications or personal experiences.
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 17W.1)
-
Fach: Informationstechnik
(Wahlfach)
-
2.6 Bildverarbeitung (
2.0h VC / 3.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 3.0 ECTS) Absolvierung im 5. Semester empfohlen
-
2.6 Bildverarbeitung (
2.0h VC / 3.0 ECTS)
-
Fach: Informationstechnik
(Wahlfach)
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 12W.1)
-
Fach: Informationstechnik
(Wahlfach)
-
Bildverarbeitung (
2.0h VO / 3.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
-
Bildverarbeitung (
2.0h VO / 3.0 ECTS)
-
Fach: Informationstechnik
(Wahlfach)
- Bachelorstudium Informationstechnik
(SKZ: 289, Version: 22W.1)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
-
11a.4 Ausgewählte LVen der Informationstechnik: Chip Design, Einf.in die Multimedia-Technik, Fundamentals of Image Processing, Measurement Signal Processing, Mobile Robot Programming, Systemsicherheit (
0.0h VO, VC, KS, UE / 6.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS) Absolvierung im 3., 4., 5., 6. Semester empfohlen
-
11a.4 Ausgewählte LVen der Informationstechnik: Chip Design, Einf.in die Multimedia-Technik, Fundamentals of Image Processing, Measurement Signal Processing, Mobile Robot Programming, Systemsicherheit (
0.0h VO, VC, KS, UE / 6.0 ECTS)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
- Bachelorstudium Informationstechnik
(SKZ: 289, Version: 12W.2)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
-
Wahl von Lehrveranstaltungen (
0.0h VK/VO/KU / 6.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 3.0 ECTS)
-
Wahl von Lehrveranstaltungen (
0.0h VK/VO/KU / 6.0 ECTS)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: ICE- Supplements
(Wahlfach)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: ICE- Supplements
(Wahlfach)
- Masterstudium Mathematics
(SKZ: 401, Version: 18W.1)
-
Fach: Information and Communications Engineering
(Wahlfach)
-
9.4 Fundamentals of Image Processing (
2.0h VC / 4.0 ECTS)
- 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
-
9.4 Fundamentals of Image Processing (
2.0h VC / 4.0 ECTS)
-
Fach: Information and Communications Engineering
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
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Wintersemester 2022/23
- 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2021/22
- 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2020/21
- 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2019/20
- 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2018/19
- 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2017/18
- 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2016/17
- 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2015/16
- 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2014/15
- 700.304 VK Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2013/14
- 700.304 VK Fundamentals of Image Processing (2.0h / 4.0ECTS)
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Wintersemester 2012/13
- 700.304 VK Fundamentals of Image Processing (2.0h / 4.0ECTS)