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Luc Van Gool: Katalogdaten im Herbstsemester 2018

NameHerr Prof. Dr. Luc Van Gool
LehrgebietComputer Vision
Adresse
Institut für Bildverarbeitung
ETH Zürich, ETF C 117
Sternwartstrasse 7
8092 Zürich
SWITZERLAND
Telefon+41 44 632 65 78
E-Mailvangool@vision.ee.ethz.ch
DepartementInformationstechnologie und Elektrotechnik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information 6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight 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.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThis 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.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux.
The course language is English.
227-0919-00LKnowledge-Based Image Interpretation Information 0 KP2SL. Van Gool
KurzbeschreibungWith the lecture series on special topics of Knowledge based image interpretation we sporadically offer special talks.
LernzielTo become acquainted with selected, recent results in image analysis and interpretation.
263-5902-00LComputer Vision Information 6 KP3V + 1U + 1AM. Pollefeys, V. Ferrari, L. Van Gool
KurzbeschreibungThe 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.
LernzielThe 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.
InhaltCamera 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
Voraussetzungen / BesonderesIt is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course.