Strukturbaum
Keine Einordnung ins Vorlesungsverzeichnis vorhanden.
Veranstaltung ist aus dem Semester
SS 2019
, Aktuelles Semester: SoSe 2024
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Computer Vision (ersetzt Digitale Bildverarbeitung IN) Sprache: Englisch Belegpflicht | |||||||||||
Nr.: 7781 Vorlesung SS 2019 4 SWS Jedes Semester | |||||||||||
Fakultät: | Fakultät Elektrotechnik und Informatik | ||||||||||
Mechatronics, Abschluss 90, ( 1. - 3. Semester ) - ECTS-Punkte : 5 - Kategorie : Wahlfach | |||||||||||
Electrical Engineering and Embedded Systems, Abschluss 90, ( 1. - 3. Semester ) - ECTS-Punkte : 5 - Kategorie : Wahlfach | |||||||||||
Profil IN-Künstliche Intelligenz und Autonme Rob., Abschluss 90, ( 1. - 3. Semester ) - ECTS-Punkte : 5 - Kategorie : Wahlpflichtfach | |||||||||||
Profil IN-Spiele, Abschluss 90, ( 1. - 3. Semester ) - ECTS-Punkte : 5 - Kategorie : Wahlfach | |||||||||||
Profil IN-IT-Sicherheit, Abschluss 90, ( 1. - 3. Semester ) - ECTS-Punkte : 5 - Kategorie : Wahlfach | |||||||||||
Zugeordnete Lehrperson: | Elser | ||||||||||
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Termin: | Dienstag 08:00 - 11:15 wöchentl | Raum : T 117 Gebäude T | |||||||||
Dienstag 08:00 - 09:30 wöchentl | Raum : K 102 Gebäude K | ||||||||||
Inhalt: | Overview of the content:
1. Brief introduction 2. The pinhole camera model 3. Recognition 4. Motion analysis 5. 3D reconstruction We will focus on automotive applications and take a look at both, traditional and machine learning algorithms. To evaluate some of these, algorithms, we will take a look at different datasets (MS COCO, KITTI, ApolloScpae, etc). These datasets can also be used as a benchmark for our projects. For traditional algortihms, we will work with OpenCV. For machine learning algorithms, we will take a look at the TensorFlow Object Detection API. As part of this course, you will implement or evaluate one of these algorithms using C++ or Python. The algorithms will either have to work on already recorded data (like the datasets above) or a given sensor. |
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Literatur: | R. Szeliski: Computer Vision: Algorithms and Applications
http://szeliski.org/Book/ OpenCV tutorials (C++, Python) https://docs.opencv.org/trunk/ Tensorflow Object Detection API https://github.com/tensorflow/models/tree/master/research/object_detection |
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Lernziele: | After attending the lecture, the participants will be able to understand the most important algorithms for three main tasks in computer vision:
1. Recognition: object detection, pose estimation, etc. 2. Motion analysis: egomotion, optical flow, etc. 3. 3D reconstruction: localization, mapping, using mono camera and stereo vision At the end of the semester, the participants will implement or evaluate one of these algorithms using C++ or Python. |
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Voraussetzungen: | Good understanding of mathematics in general.
Good understanding of at least one programming language, preferable Python or C++. Depending on your project: additional knowledge and first experiences with machine learning using TensorFlow or comparable frameworks. |
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Leistungsnachweis: | PA benotet
Master Informatik: anstelle von Digitaler Bildverarbeitung mit K90 oder PF Aushang ab SoSe19: PA benotet |
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