Orçun Göksel: Catalogue data in Autumn Semester 2018
|Name||Prof. Dr. Orçun Göksel|
|Field||Computer-assisted Applications in Medicine|
Computergest. Anwend. in Medizin
ETH Zürich, ETF C 107
|Telephone||+41 44 632 25 29|
|Department||Information Technology and Electrical Engineering|
|227-0447-00L||Image Analysis and Computer Vision||6 credits||3V + 1U||L. Van Gool, O. Göksel, E. Konukoglu|
|Abstract||Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks.|
|Objective||Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.|
|Content||This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning.|
The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer.
The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given.
|Lecture notes||Course material Script, computer demonstrations, exercises and problem solutions|
|Prerequisites / Notice||Prerequisites: |
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux.
The course language is English.
|376-1279-00L||Virtual and Augmented Reality in Medicine||3 credits||2V||R. Riener, O. Göksel, M. Harders|
|Abstract||Virtual and Augmented Reality can support applications in medicine, e.g. for training, planning or therapy. This lecture derives the technical principles of multimodal (audiovisual, haptic, etc.) input devices, displays, and rendering techniques. Examples are presented in the fields of surgical training, intra-operative support, and rehabilitation. The lecture is accompanied by lab demonstrations.|
|Objective||Provide theoretical and practical knowledge of new principles and applications of multi-modal simulation and interface technologies in medical education, therapy, and rehabilitation.|
|Content||Virtual and Augmented Reality have the potential to provide descriptive and practical information for medical applications, while relieving the patient and/or the physician. Multi-modal interactions between the user and the virtual environment facilitate the generation of high-fidelity sensory impressions, by using visual, haptic, and auditory modalities. On the basis of the existing physiological constraints, this lecture derives the technical requirements and principles of multi-modal input devices, displays, and rendering techniques. Several examples are presented that are currently being developed or already applied, for instance in surgical training, intra-operative augmentation, and rehabilitation. The lecture will be accompanied by visits to facilities equipped with current VR and AR equipment.|
|Literature||Recommended readings will be announced in the lecture. Selected books covering some of the presented topics are: |
• Virtual Reality in Medicine. Riener, Robert; Harders, Matthias; 2012 Springer.
• Augmented Reality: Principles and Practice (Usability). Schmalstieg, Dieter; Hollerer, Tobias; 2016 Pearson.
• Real-Time Volume Graphics. Rezk-Salama, Christof; Engel, Klaus; Hadwiger, Markus; Kniss, Joe; Weiskopf, Daniel; 2006 Taylor & Francis.
• Haptic Rendering: Foundations, Algorithms, and Applications. Lin, Ming; Otaduy, Miguel; 2008 CRC Press.
• Developing Virtual Reality Applications: Foundations of Effective Design. Craig , Alan; Sherman, William; Will, Jeffrey; 2009 Morgan Kaufmann.
|Prerequisites / Notice||Notice The course language is English. Any further details will be announced in the first lecture. |
The general target group is students of higher semesters as well as PhD students of D-HEST, D-MAVT, D-ITET, D-INFK, D-PHYS. Students of other departments, faculties, and courses are also welcome.