700.304 (23W) Fundamentals of Image Processing

zu Moodle

Wintersemester 2023/24

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Erster Termin der LV
18.10.2023 13:00 - 17:00 On Campus

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Überblick

Lehrende/r

Assoc. Prof. Dr. techn. habil. Fadi Al Machot

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

Institut für Intelligente Systemtechnologien

Unterrichtssprache

Englisch

LV-Beginn

18.10.2023

eLearning

zum Moodle-Kurs

Seniorstudium Liberale

Zeit und Ort

Liste der Termine wird geladen...

LV-Beschreibung

Deutsch

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

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Englisch

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 Benotungsschema

Position 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

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)

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

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)

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)

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)

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)

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)

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)

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)

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)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Wintersemester 2022/23

700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2021/22

700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2020/21

700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2019/20

700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2018/19

700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2017/18

700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2016/17

700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2015/16

700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2014/15

700.304 VK Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2013/14

700.304 VK Fundamentals of Image Processing (2.0h / 4.0ECTS)

Wintersemester 2012/13

700.304 VK Fundamentals of Image Processing (2.0h / 4.0ECTS)