(In English)

Digital Image Processing

... with applications in computer vision and machine learning (Summer Semester)

Due to the current COVID-19 outbreak no in-class sessions will be held until further notice. However, video recordings of the lectures as well as annotated lecture notes will be provided on ILIAS.

Synopsis

This course is directed to students in their Master's curriculum and covers digital image processing with applications in computer vision and machine learning. The first part of the course discusses fundamentals in digital image processing including image acquisition and representation techniques, image sampling and quantization, point and morphological image operations, image filtering and correlation, noise reduction and restauration. The second part of the course will present issues and technologies used in modern image processing or computer vision systems. This includes image-based feature extraction and matching, segmentation, motion estimation and classification as well as an overview on state-of-the-art techniques making use of (Deep) Neural Networks. Course units are supplemented by practical application examples from industry. Basic knowledge in linear algebra, probability and system theory, as well as basic programming skills are recommended for this course.

Contents and Educational Objectives

Content Overview
  • Introduction and Motivation
  • Image Acquisition and Representation
  • Fourier Transformation and 2D linear systems
  • Image Operations and Image Filtering
  • Feature Extraction and Matching
  • Segmentation
  • Motion Estimation
  • Classification
  • Neural Networks
  • Deep Neural Networks and Applications
Course goals

At the end of the course the student will be able to

  1. Handle techniques of image acquisition, representation and approximation of images in
    order to extract their meaningful components for a particular application.
  2. Apply image operations and filters to isolate certain frequency components or to cancel out
    image noises.
  3. Extract, register and match structures of interest in an image, such as contours or key
    features.
  4. Segment an image into regions of homogeneous characteristics, targeting semantic
    interpretation of the image content.
  5. Estimate 3D structures and object motion from video sequences.
  6. Detect and track objects of interest in single images or video sequences.
  7. Design and create (Deep) Neural Network Architectures for various tasks, ranging from
    image segmentation to object detection.
  8. Identify solutions to complex problems across different application domains, such as quality
    control, video surveillance, intelligent vehicles and human-machine interfaces.

Course Information

3 ECTS credits. Course given in English language.

Lecturer Dr.-Ing. Fabian Flohr
Time Slot Wednesday, 14:00-15:30
Lecture Hall PW47, 2.314 (INÜ Auditorium)
Weekly Credit Hours 2
Dr.-Ing.

Fabian Flohr

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