Konrad Schindler: Katalogdaten im Frühjahrssemester 2021 |
| Name | Herr Prof. Dr. Konrad Schindler |
| Lehrgebiet | Photogrammetrie |
| Adresse | I. f. Geodäsie u. Photogrammetrie ETH Zürich, HIL D 42.3 Stefano-Franscini-Platz 5 8093 Zürich SWITZERLAND |
| Telefon | +41 44 633 30 04 |
| schindler@ethz.ch | |
| URL | https://igp.ethz.ch/personen/person-detail.html?persid=143986 |
| Departement | Bau, Umwelt und Geomatik |
| Beziehung | Ordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  Number of participants limited to 21. | 1 KP | 2S | B. Soja, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, M. J. Van Strien | |
| Kurzbeschreibung | Current research in machine learning and data science within the research fields of the department. The goal is to learn about current research projects at our department, to strengthen our expertise and collaboration with respect to data-driven models and methods, to provide a platform where research challenges can be discussed, and also to practice scientific presentations. | ||||
| Lernziel | - learn about discipline-specific methods and applications of data science in neighbouring fields - network people and methodological expertise across disciplines - establish links and discuss connections, common challenges and disciplinespecific differences - practice presentation and discussion of technical content to a broader, less specialised scientific audience | ||||
| Inhalt | Current research at D-BAUG will be presented and discussed. | ||||
| Voraussetzungen / Besonderes | This doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar. Participants are expected to possess elementary skills in statistics, data science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects. | ||||
| 101-0523-11L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (FS21) Number of participants limited to 21. | 1 KP | 2S | M. Lukovic, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, K. Schindler, B. Soja, M. J. Van Strien | |
| Kurzbeschreibung | This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms). | ||||
| Lernziel | Students will • Critically read scientific papers on the recent developments in machine learning • Put the research in context • Present the contributions • Discuss the validity of the scientific approach • Evaluate the underlying assumptions • Evaluate the transferability/adpatability of the proposed approaches to own research • (Optionally) implement the proposed approaches. | ||||
| Inhalt | With the increasing amount of data collected in various domains, the importance of data science in many disciplines, such as infrastructure monitoring and management, transportation, spatial planning, structural and environmental engineering, has been increasing. The field is constantly developing further with numerous advances, extensions and modifications. The course aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms). Each student will select a paper that is relevant for his/her research and present its content in the seminar, putting it into context, analyzing the assumptions, the transferability and generalizability of the proposed approaches. The students will also link the research content of the selected paper to the own research, evaluating the potential of transferring or adapting it. If possible and applicable, the students will also implement the adapted algorithms The students will work in groups of three students, where each of the three students will be reading each other’s selected papers and providing feedback to each other. | ||||
| Voraussetzungen / Besonderes | This doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar. Participants are expected to possess elementary skills in statistics, data science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects. | ||||
| 103-0254-AAL | Photogrammetry Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben. Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen. | 6 KP | 13R | K. Schindler | |
| Kurzbeschreibung | The class conveys the basics of photogrammetry. It shall equip students with basic knowledge of the principles, methods and applications of image-based measurement. | ||||
| Lernziel | Understanding 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. | ||||
| Inhalt | Fundamental concepts 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 3D reconstruction and stereoscopy; digital photogrammetric workstations; recording geometry and flight planning | ||||
| Skript | Photogrammetry - Basics (slides on the web) Exercise material (on the web) | ||||
| Literatur | - 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 | ||||
| Voraussetzungen / Besonderes | Requirements: knowledge of physics, linear algebra and analytical geometry, calculus, least-squares adjustment and statistics, basic programming skills. | ||||
| 103-0274-AAL | Image Processing Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben. Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen. | 3 KP | 6R | K. Schindler, J. D. Wegner | |
| Kurzbeschreibung | The objective of this lecture is to introduce the basic concepts of image formation and explain the basic methods of signal and image processing. | ||||
| Lernziel | Understanding 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. | ||||
| Inhalt | The 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 | ||||
| Skript | A script will be provided as PDF files on the lecture website. | ||||
| Literatur | We 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 | ||||
| Voraussetzungen / Besonderes | The lecture is accompanied by programming assignments, that need to be completed in order to pass the course. | ||||
| 103-0274-01L | Bildverarbeitung | 3 KP | 2G | K. Schindler, J. D. Wegner | |
| Kurzbeschreibung | Einfuehrung in die grundlegenden Konzepte und Methoden der digitalen Bildverarbeitung. | ||||
| Lernziel | - Mathematische und statistischen Modellierung von Bildern - Beherrschung grundlegender Methoden der digitalen Bild- und Signalverarbeitung - Kenntnis wichtiger Bildverarbeitungsoperationen und -algorithmen - Auswahl und Anwendung der vorgestellten Rechenwerkzeuge zum Lösen von Bildverarbeitungsaufgaben - Verständnis der computer-gestützten Bildverarbeitung als Grundlage von Fernerkundung, Photogrammetrie und computer vision | ||||
| Inhalt | • 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 | ||||
| 103-0798-00L | Geodetic Project Course Number of participants limited to 24. | 5 KP | 9P | M. Rothacher, K. Schindler, A. Wieser | |
| Kurzbeschreibung | Dreiwöchige Arbeit an einem geodätischen Projekt mit Praxisbezug | ||||
| Lernziel | Dreiwöchige Arbeit an einem geodätischen Projekt mit Praxisbezug | ||||
| Inhalt | Gruppenweise, selbständige Bearbeitung aktueller Vermessungsprojekte und Erstellung eines Technischen Berichtes (Projektbeschreibung, Auswertung, Resultate und Interpretationen), Möglichkeit der Weiterführung in Master- oder Projektarbeiten. | ||||
| Voraussetzungen / Besonderes | Der Kurs findet zwischen 14.6. und 9.7.2021 statt. Innerhalb dieser Zeit finden voraussichtlich während ca. 2 Wochen Feldarbeiten in Graubünden statt. Darüber hinaus finden Vor- und Nacharbeiten in Zürich statt. | ||||
| 103-0848-00L | Industrial Metrology and Machine Vision Number of participants limited to 30. | 4 KP | 3G | K. Schindler, D. Salido Monzú | |
| Kurzbeschreibung | This course introduces contact and non-contact techniques for 3D coordinate, shape and motion determination as used for 3D inspection, dimensional control, reverse engineering, motion capture and similar industrial applications. | ||||
| Lernziel | Understanding the physical basis of photographic sensors and imaging; familiarization with a broader view of image-based 3D geometry estimation beyond the classical photogrammetric approach; understanding the concepts of measurement traceability and uncertainty; acquiring an overview of general 3D image metrology including contact and non-contact techniques (coordinate measurement machines; optical tooling; laser-based high-precision instruments). | ||||
| Inhalt | CCD and CMOS technology; structured light and active stereo; shading models, shape from shading and photometric stereo; shape from focus; laser interferometry, laser tracker, laser radar; contact and non-contact coordinate measurement machines; optical tooling; measurement traceability, measurement uncertainty, calibration of measurement systems; 3d surface representations; case studies. | ||||
| Skript | Lecture slides and further literature will be made available on the course webpage. | ||||
| 103-0849-00L | Multivariate Statistik und Machine Learning Maximale Teilnehmerzahl: 40 | 4 KP | 4G | K. Schindler | |
| Kurzbeschreibung | Einfuehrung in statistische Modellierung und maschinelles Lernen. | ||||
| Lernziel | Ziel ist es, das Prinzip und die die Werkzeuge des maschinellen Lernens kennenzulernen, und sie zur Datenalalyse in praktischen Situationen anwenden zu koennen. | ||||
| Inhalt | Multivariate Verteilungen; Vergleich von Wahrscheinlichkeitsverteilungen; Regression; Klassifizierung; Modellselektion und cross-validation; Clustering und Dichteschaetzung; mixture models; neuronale Netze | ||||
| Literatur | C. Bishop: Pattern Recognition and Machine Learning, Springer 2006 T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer 2017 R. Duda, P. Hart, D. Stork: Pattern Classification, Wiley 2000 | ||||
| 103-0851-00L | Photogrammetrie | 6 KP | 4G | K. Schindler | |
| Kurzbeschreibung | Die Veranstaltung vermittelt die Grundlagen der Photogrammetrie. | ||||
| Lernziel | Ziel ist ein detailliertes Verstaendnis der Prinzipien, Methoden und Anwendungen der bildbasierten 3D-Vermessung. | ||||
| Inhalt | Die Grundlagen der Photogrammetrie und ihre Produkte und Anwendungen: das Prinzip der bildbasierten Vermessung; digitale Luftbildkameras und verwandte Sensoren; projektive Geometrie; mathematische Beschreibung, Kalibrierung und Orientierung von Kameras; photogrammetrische Triangulierung und Flaechenrekonstruktion; Orthophoto-Herstellung; Buendelausgleichung; Aufnahmegeometrie und Bildflugplanung; flugzeuggestuetztes Laser-Scanning | ||||
| Literatur | - Wolfgang Foerstner and Bernahrd Wrobel: Photogrammetric Computer Vision, Springer, 2016 - Thomas Luhmann: Nahbereichsphotogrammetrie. Grundlagen, Methoden, Beispiele, Wichmann Verlag, 4. Auflage 2018 - Richard Hartley and Andrew Zisserman: Multiple View Geometry, Cambridge University Press; 2. Auflage 2004 | ||||