Konrad Schindler: Catalogue data in Autumn Semester 2014

Name Prof. Dr. Konrad Schindler
FieldPhotogrammetry
Address
I. f. Geodäsie u. Photogrammetrie
ETH Zürich, HIL D 42.3
Stefano-Franscini-Platz 5
8093 Zürich
SWITZERLAND
Telephone+41 44 633 30 04
E-mailschindler@ethz.ch
URLhttps://igp.ethz.ch/personen/person-detail.html?persid=143986
DepartmentCivil, Environmental and Geomatic Engineering
RelationshipFull Professor

NumberTitleECTSHoursLecturers
103-0254-AALPhotogrammetry
Enrolment only for MSc students who need this course as additional requirement.
5 credits4RK. Schindler
AbstractThe class conveys the basics of photogrammetry. Its aim is to equip students with an understanding of the principles, methods and applications of image-based measurement.
ObjectiveThe aim is an understanding of the principles, methods and possible applications of photogrammetry. The course also forms the basis for more in-depth studies and self-reliant photogrammetric project work in further photogrammetry courses.
ContentThe basics of photogrammetry, its products and applications: the principle of image-based measurement; digital aerial cameras and related sensors; projective geometry; mathematical modeling, calibration and orientation of cameras; photogrammetric reconstruction of points and lines, and stereoscopy; orthophoto generation; digital photogrammetric workstations; recording geometry and flight planning
Lecture notesPhotogrammetry (slides on the web)
Literature- Kraus, K.: Photogrammetrie, Band 1: Geometrische Informationen aus Photographien und Laserscanneraufnahmen, mit Beiträgen von Peter Waldhäusl, Walter de Gruyter Verlag, Berlin, 7th edition
- Kraus, K.: Photogrammetrie, Band 2: Verfeinerte Methoden und Anwendungen, mit Beiträgen von J. Jansa und H. Kager, Walter de Gruyter Verlag, Berlin, 3rd edition
- Thomas Luhmann: Nahbereichsphotogrammetrie. Grundlagen, Methoden und Anwendungen, H. Wichmann Verlag, Karlsruhe, 2nd edition 2003
- Richard Hartley and Andrew Zisserman: Multiple View Geometry, Cambridge University Press; 2nd edition 2004
Prerequisites / NoticeRequirements: knowledge of physics, linear algebra and analytical geometry, calculus, least-squares adjustment and statistics, basic programming skills.
103-0267-01LPhotogrammetry and 3D Vision Lab
It is suggested that students take the course "Photogrammetrie" at bachelor level before this one.
3 credits2PK. Schindler
AbstractThe course deals with selected topics of close-range photogrammetry and geometric computer vision, including wide-baseline image matching and reconstruction, dense surface reconstruction, panorama stitching and image indexing; emphasis is put on practical project work.
ObjectiveThe aim of the course is to get to know the methods and practice of close-range photogrammetric reconstruction, and an in-depth understanding of selected topics in modern close-range photogrammetry and computer vision.
ContentThis course builds in part on the courses "Photogrammetrie", "Bildverarbeitung" and "Photogrammetrie II" from the Bachelor program. It focusses on the particular challenges of automated close-range photogrammetry.
Lecture notesPresentation slides, necessary publications and complementary learning materials will be provided through a dedicated course web-site.
LiteratureRecommended textbooks:
- T. Luhmann. Nahbereichsphotogrammetrie (also available in English )
- R. Hartley and A. Zisserman. Multi-view geometry in computer vision
- R. Szeliski. Computer Vision
Prerequisites / NoticeA recommended prerequisite for taking this course are the Bachelor courses "Photogrammetrie", "Bildverarbeitung" and "Photogrammetrie II". If you have not passed them, please contact the main lecturer of the course before enrolling. The course will include both practical work with commercial software, and programming in Matlab.
103-0287-00LImage Interpretation4 credits3GK. Schindler
AbstractIntroduction to interactive, semi-automatic and automatic methods for image interpretation; methodological aspects of computer-assisted remote sensing, including semantic image classification and segmentation; detection and extraction of individual objects; estimation of physical parameters.
ObjectiveUnderstanding the tasks, problems, and applications of image interpretation; basic introduction of computational methods for image-based classification and parameter estimation (clustering, classification, regression), with focus on remote sensing.
ContentImage (and point-cloud) interpretation tasks: semantic classification (e.g. land-cover mapping), physical parameter estimation (e.g. forest biomass), object extraction (e.g. roads, buildings), visual driver assistance;
Image coding and features; probabilistic inference, generative and discriminative models; clustering and segmentation; continuous parameter estimation, regression; classification and labeling; atmospheric influences in satellite remote sensing;
LiteratureJ. A. Richards: Remote Sensing Digital Image Analysis - An Introduction
C. Bishop: Pattern Recognition and Machine Learning
Prerequisites / Noticebasics of probability theory and statistics; basics of image processing; elementary programming skills (Matlab);
103-0817-00LGeomatics Seminar Restricted registration - show details 4 credits2SM. Rothacher, K. W. Axhausen, A. Geiger, A. Grêt-Regamey, L. Hurni, M. Raubal, K. Schindler, B. Scholl, U. A. Weidmann, A. Wieser
AbstractIntroduction to general scientific working methods and skills in the core fields of geomatics. It includes a literature study, a review of one of the articles, a presentation and a report about the literature study.
ObjectiveLearn how to search for literature, how to write a scientific report, how to present scientific results, and how to critically read and review a scientific article
ContentA list of themes for the literature study are made availabel at the beginning of the semester. A theme can be selected based on a moodle.
Prerequisites / NoticeAgreement with one of the responsible Professors is necessary