Fisher Yu: Catalogue data in Autumn Semester 2022 |
| Name | Prof. Dr. Fisher Yu |
| Field | Computer Vision |
| Address | Professur für Computer Vision ETH Zürich, ETF F 104 Sternwartstrasse 7 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 55 67 |
| fisheryu@ethz.ch | |
| URL | http://yf.io |
| Department | Information Technology and Electrical Engineering |
| Relationship | Assistant Professor (Tenure Track) |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 227-0085-24L | Projects & Seminars: Vision and Control in RoboCup  Only for Electrical Engineering and Information Technology BSc. 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 | Vision and Control in RoboCup 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, where the focus lies on developing robust and efficient algorithms for vision, control and behavior. The main objective of this course is to familiarize students on the fundamental challenges we encounter in RoboCup. This is accomplished by a combination of lecture sessions, related student exercise sets and programming projects in MATLAB and Python. The topics cover visual localization, deep learning for object detection and reinforcement learning control of unknown systems. Important information for candidates: You are required to bring your own Laptop for the programing exercises. The course is taught in English and open to 5th or higher semester students. A basic knowledge of programming in Python and MATLAB is required. A prior exposure to control theory (e.g. by attending a Control Systems course) is desirable. Those students who are not familiar with control theory will need to complete some extra study to understand some aspects of this P&S. | ||||
| 227-0085-31L | Projects & Seminars: Vision Goes Vegas Only for Electrical Engineering and Information Technology BSc. 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. | ||||
| 227-0447-00L | Image Analysis and Computer Vision  | 6 credits | 3V + 1U | E. Konukoglu, F. Yu | |
| Abstract | Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks. | ||||
| Learning objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | ||||
| Content | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | ||||
| Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | ||||
| Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | ||||
| 263-5902-00L | Computer Vision | 8 credits | 3V + 1U + 3A | M. Pollefeys, S. Tang, F. Yu | |
| Abstract | The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises. | ||||
| Learning objective | The objectives of this course are: 1. To introduce the fundamental problems of computer vision. 2. To introduce the main concepts and techniques used to solve those. 3. To enable participants to implement solutions for reasonably complex problems. 4. To enable participants to make sense of the computer vision literature. | ||||
| Content | Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition | ||||
| Prerequisites / Notice | It is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course. | ||||