Luc Van Gool: Catalogue data in Autumn Semester 2023 |
| Name | Prof. em. Dr. Luc Van Gool |
| Field | Computer Vision |
| Address | Institut für Bildverarbeitung ETH Zürich, ETF C 117 Sternwartstrasse 7 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 65 78 |
| vangool@vision.ee.ethz.ch | |
| Department | Information Technology and Electrical Engineering |
| Relationship | Professor emeritus |
| Number | Title | ECTS | Hours | Lecturers | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227-0085-11L | P&S: Deep Learning for Image Manipulation (DLIM)  The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. | 3 credits | 3P | L. Van Gool | ||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||
| Learning objective | Deep Learning – Image Manipulation – Image Enhancement – Image Restoration – Style Transfer – Image to Image Translation – Generative Models – TensorFlow/PyTorch – Projects With the advent of deep learning tremendous advances were achieved in numerous areas from computer vision, computer graphics, and image processing. Using these techniques, an image can be automatically manipulated in various ways with high-quality results, often fooling the human observer. Deep learning based image processing and manipulation are being applied in a vast number of emerging technologies, including image enhancement in smartphone cameras, automated image editing, image content creation, graphics, and autonomous driving. This course focuses on the fundamentals of deep learning and image manipulation. Students will learn the tools to implement and develop deep learning solutions for a variety of image manipulation tasks. The course will end with a 4 weeks project where the students can target a specific application scenario. The course will be taught in English. | |||||||||||
| 227-0085-24L | P&S: RoboCup: Learning and Control The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. | 3 credits | 1P | J. Lygeros, L. Van Gool, F. Yu | ||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||
| Learning objective | "RoboCup: Learning and Control" is jointly offered by Prof. John Lygeros (IfA), Prof. Luc Van Gool (CVL) and Prof. Fisher Yu (CVL). RoboCup is a tournament where teams of autonomous robots compete in soccer matches against each other. The ETH team NomadZ (https://robocup.ethz.ch/) plays in the Standard Platform League with a team of humanoid NAO robots. The focus lies on developing robust and efficient algorithms for vision, control and behavior. The main objective of this course is for students to become familiar with theoretical aspects currently in the spotlight of RoboCup. This is accomplished by a combination of theory sessions, related student exercise sets and programming projects in MATLAB, Python, and C++. The topics cover fundamental topics on data-driven learning and control. | |||||||||||
| Prerequisites / Notice | Important information for candidates: You are required to bring your own Laptop for the programming exercises. A basic knowledge of programming in MATLAB, Python, and C++ is required. The course is taught in English and is open to 5th or higher-semester students. Prior exposure to control theory (e.g., by attending a Control Systems course) is desirable but not required. Students who are not familiar with control theory will need some extra study to understand some aspects of this P&S course. | |||||||||||
Competencies |
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| 227-0085-31L | P&S: Vision Goes Vegas The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. | 2 credits | 2P | L. Van Gool, F. Yu | ||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||
| Learning objective | Computer Vision beschäftigt sich unter anderem damit, Maschinen zu befähigen ihre Umwelt zu sehen und das wahrgenommene Bild zu verstehen. In unserem Projekt soll ein System entwickelt werden, das Spielkarten erkennen kann und, einer guten Strategie folgend, erfolgreich Black-Jack spielen kann. Die Teilnehmer des Projektes werden kleine Teams bilden und gemeinsam mit einem Assistenten die Aufgabe erarbeiten und eine Implementierung erstellen. Am Ende des Semesters sollen die Programme im öffentlichen Wettstreit gegeneinander antreten! Ziel des Projektes ist es, aktuelle Methoden der Computer Vision kennen zu lernen. Spielkarten, die von einer Digitalkamera in beliebiger Orientierung aufgenommen werden, müssen registriert und erkannt werden. Ein Strategiemodul kontrolliert dann die Spieltaktik aufgrund allgemeiner Regeln und dem Wissen über schon gefallene Karten. Da sehr viele verschiedene Möglichkeiten bestehen, solch ein System zu realisieren, sind der Phantasie der Teilnehmer keine Grenzen gesetzt. Als Voraussetzungen sollte Interesse an Computer Vision mitgebracht werden und die Bereitschaft, sich in einem Team von Mitstudierenden einzubringen. Kenntnisse in C++ sind notwendig. Der Kurs wird von Prof. Fisher Yu mitbegutachtet. Dieses P&S wird in englischer Sprache durchgeführt. | |||||||||||
| 227-0919-00L | Knowledge-Based Image Interpretation | 0 credits | 2S | L. Van Gool, E. Konukoglu, F. Yu | ||||||||
| Abstract | With the lecture series on special topics of Knowledge based image interpretation we sporadically offer special talks. | |||||||||||
| Learning objective | To become acquainted with selected, recent results in image analysis and interpretation. | |||||||||||