|Lecture /w Excercise|
Tuesday, 1-3pm; Thursday, 11am-1pm G29-335
|Prof. Dr.-Ing. Klaus Tönnies, Johannes Steffen|
An introduction to image processing:
W. Burger & M.J. Burge. Digital Image Processing – An Algorithmic Introduction Using JAVA. 2nd edition, Springer, 2016
Computer Vision deals with the extraction of information from images or image sequences comprising, e.g., object detection and classification, pose estimation, activity recognition, semantic segmentation and many others. It has found numerous applications in industry and research. Solutions for most of these computer vision tasks have been established many years ago but are nowadays carried out using deep neural networks. Within this course, we will briefly review important aspects of classical, mostly high level computer vision tasks and then explore the different network topologies that have been developed for end-to-end learning solutions. A background in basic image processing or computer vision is expected.
- Features from Images
- Working with Features
- Classification with Generative Models
- Classification with Discriminative Models
- Using Neural Networks
- Deep Convolutional Neural Networks
- Learning in Deep CNNs
- Object Detection and Deep Learning
- Semantic Segmentation and Deep Learning
- Other Computer Vision Tasks (Pose Estimation, Activity Recognition, Object Tracking)
- Generative Models and Deep Learning
Courses of study:
This course is limited to 25 students only.
To be allowed to participate in the course, students have to successfully pass the admission exam. Details will be announced soon.
To be allowed to take part in the examination, students have to successfully participate in an accompanying project. Details will be discussed within the first lecture.