Jan Dirk Wegner: Catalogue data in Spring Semester 2021

Name Prof. Dr. Jan Dirk Wegner
(Professor Universität Zürich (UZH))
Address
I. f. Geodäsie u. Photogrammetrie
ETH Zürich, HIL D 44.2
Stefano-Franscini-Platz 5
8093 Zürich
SWITZERLAND
Telephone+41 44 633 68 08
E-mailjan.wegner@geod.baug.ethz.ch
DepartmentCivil, Environmental and Geomatic Engineering
RelationshipLecturer

NumberTitleECTSHoursLecturers
103-0274-AALImage Processing
Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement.

Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.
3 credits6RK. Schindler, J. D. Wegner
AbstractThe objective of this lecture is to introduce the basic concepts of image formation and explain the basic methods of signal and image processing.
ObjectiveUnderstanding core methods and algorithms in image processing and computer vision and the underlying signal processing foundations.
Applying image processing algorithms to relevant problems in photogrammetry and remote sensing.
ContentThe following topics will be covered in the course:
- Properties of digital images
- Signal processing/Sampling
- Image enhancement
- Image restoration: Spatial domain
- Image restoration: Fourier domain
- Color/Demosaicing
- Image compression
- Feature extraction
- Texture analysis
- Image segmentation
Lecture notesA script will be provided as PDF files on the lecture website.
LiteratureWe suggest the following textbooks for further reading:

Rafael C. Gonzalez, Richard E. Woods
Digital Image Processing
Prentice Hall International, 2008
ISBN: 013168728X

Rafael C. Gonzalez, Steven L. Eddins, Richard E. Woods:
Digital Image Processing Using MATLAB
Prentice Hall, 2003
ISBN: 0130085197
Prerequisites / NoticeThe lecture is accompanied by programming assignments, that need to be completed in order to pass the course.
103-0274-01LImage Processing3 credits2GK. Schindler, J. D. Wegner
AbstractIntroduction to basic concepts and methods of digital image processing.
Objective- Mathematical and statistical description of images
- knowledge of basic algorithms for digital image and signal processing
- familiarity with fundamental image processing operations
- selection and application of suitable computational methods for basic image processing tasks
- understanding of digital image processing as a basis for remote sensing, photogrammetry and computer vision
Content• Digitale Bilder, Signalprozessierung, Abtastung
• Geometrische Transformationen
• Farbräume
• Filterung von Bildern, Bildrestauration und -verbesserung
• Morphologische Operationen
• Punkt- und Liniendetektion
• Ähnlichkeitsmasse und Matching von Bildern
• Bildsegmentierung
• Radiometrie von Satellitenbildern
851-0648-00LMachine Learning for Global Development Restricted registration - show details
Number of participants limited to 24

Prerequisite: Students on BSc or MSc level who have already successfully participated in a data science and programming course.
3 credits2GJ. D. Wegner, L. Hensgen, A. Rom
AbstractIn this course students will learn theories of machine learning and its application to problems in the context of global development, with a focus on developing countries (e.g. predicting the risk of child labor or chances of a malaria outbreak). By the end of the course, students will be able to critically reflect upon linkages between technical innovations, culture and individual/societal needs.
ObjectiveThe objective of this course is to introduce students with a non-technical background to machine learning. Emphasis is on hands-on programming and implementation of basic machine learning concepts to demystify the subject, equip participants with all necessary insights and tools to develop their own solutions, and to come up with original ideas for problems related to the context of global development. Specific importance is placed upon the reconciliation of the predictions, which have been generated by automated processes, with the realities on the ground; hence the linkage between technical and social issues. This raises questions such as “In how far can we trust an algorithm?”, “Which factors are hard to measure and therefore not integrated in the algorithm but still crucial for the result, such as cultural and social influences?”. These questions will be discussed in the interdisciplinary group, equipping students with various perspectives on this crucial and very current debate.
ContentThis course will give an introduction to machine learning with emphasis on global development. We will discuss topics like data preprocessing, feature extraction, clustering, regression, classification and take some first steps towards modern deep learning. The course will consist of 50% lectures and 50% hands-on programming in python, where students will directly implement learned theory as a software to help solving problems in global development.
Prerequisites / NoticeThis course will give an introduction to machine learning with emphasis on applications in global development. It will consist of 50% lectures and 50% programming exercises (in python). Teaching assistants from the EcoVision Lab will help with all programming exercises without any needs for additional funding.

Students should bring their laptops to the exercises because we will program on laptops directly.

It is required that students enrolling in this course have successfully passed a course that deals with basic data science and are familiar with programming (preferably in Python).