Search result: Catalogue data in Spring Semester 2021
| Application Area|
Only necessary and eligible for the Master degree in Applied Mathematics.
One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area.
|Image Processing and Computer Vision|
|102-0617-01L||Methodologies for Image Processing of Remote Sensing Data||W||3 credits||2G||I. Hajnsek, O. Frey, S. Li|
|Abstract||The aim of this course is to get an overview of several methodologies/algorithms for analysis of different sensor specific information products. It is focused at students that like to deepen their knowledge and understanding of remote sensing for environmental applications.|
|Objective||The course is divided into two main parts, starting with a brief introduction to remote sensing imaging (4 lectures), and is followed by an introduction to different methodologies (8 lectures) for the quantitative estimation of bio-/geo-physical parameters. The main idea is to deepen the knowledge in remote sensing tools in order to be able to understand the information products, with respect to quality and accuracy.|
|Content||Each lecture will be composed of two parts:|
Theory: During the first hour, we go trough the main concepts needed to understand the specific algorithm.
Practice: During the second hour, the student will test/develop the actual algorithm over some real datasets using Matlab. The student will not be asked to write all the code from scratch (especially during the first lectures), but we will provide some script with missing parts or pseudo-code. However, in the later lectures the student is supposed to build up some working libraries.
|Lecture notes||Handouts for each topic will be provided.|
T. M. Lillesand, R.W. Kiefer, J.W. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons Verlag, 2008
J. R. Jensen, Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall Series in Geograpic Information Science, 2000
|227-0391-00L||Medical Image Analysis|
Basic knowledge of computer vision would be helpful.
|W||3 credits||2G||E. Konukoglu, M. A. Reyes Aguirre|
|Abstract||It is the objective of this lecture to introduce the basic concepts used |
in Medical Image Analysis. In particular the lecture focuses on shape
representation schemes, segmentation techniques, machine learning based predictive models and various image registration methods commonly used in Medical Image Analysis applications.
|Objective||This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas.|
|Prerequisites / Notice||Prerequisites: |
Basic concepts of mathematical analysis and linear algebra.
Basic knowledge of computer vision and machine learning would be helpful.
The course will be held in English.
|227-0396-00L||EXCITE Interdisciplinary Summer School on Bio-Medical Imaging |
The school admits 60 MSc or PhD students with backgrounds in biology, chemistry, mathematics, physics, computer science or engineering based on a selection process.
Students have to apply for acceptance. To apply a curriculum vitae and an application letter need to be submitted.
Further information can be found at: www.excite.ethz.ch.
|W||4 credits||6G||S. Kozerke, G. Csúcs, J. Klohs-Füchtemeier, S. F. Noerrelykke, M. P. Wolf|
|Abstract||Two-week summer school organized by EXCITE (Center for EXperimental & Clinical Imaging TEchnologies Zurich) on biological and medical imaging. The course covers X-ray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, optoacoustic imaging, infrared and optical microscopy, electron microscopy, image processing and analysis.|
|Objective||Students understand basic concepts and implementations of biological and medical imaging. Based on relative advantages and limitations of each method they can identify preferred procedures and applications. Common foundations and conceptual differences of the methods can be explained.|
|Content||Two-week summer school on biological and medical imaging. The course covers concepts and implementations of X-ray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, optoacoustic imaging, infrared and optical microscopy and electron microscopy. Multi-modal and multi-scale imaging and supporting technologies such as image analysis and modeling are discussed. Dedicated modules for physical and life scientists taking into account the various backgrounds are offered.|
|Lecture notes||Presentation slides, Web links|
|Prerequisites / Notice||The school admits 60 MSc or PhD students with backgrounds in biology, chemistry, mathematics, physics, computer science or engineering based on a selection process. To apply a curriculum vitae, a statement of purpose and applicants references need to be submitted. Further information can be found at: http://www.excite.ethz.ch/education/summer-school.html|
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