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#40993 / #1

Seit SoSe 2020

English

Event-based Robot Vision

6

Gallego, Guillermo

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Technische Informatik und Mikroelektronik

34342000 FG Robotic Interactive Perception

No information

Kontakt


MAR 5-5

No information

guillermo.gallego@tu-berlin.de

Learning Outcomes

Participants will learn basic concepts, theoretical foundations and relevant algorithms developed in the field of event-based (i.e., neuromorphic) vision. Upon completing the module, participants will have an overview of the field, spanning from the principle of operation of event-based sensors (e.g., event-based cameras), their advantages and disadvantages, to the methods used to process their output for a target application. Participants will also be aware of the differences with standard (frame-based) computer vision, in terms of methods, performance criteria and applications.

Content

This course is the first of its kind, worldwide. To the best of the instructor's knowledge, no similar course has been offered anywhere due to the novelty of the topics covered, which have appeared in research conferences and journals over the last ten years. The topics covered include the following: Bio-inspired principle of operation of event-based (i.e., neuromorphic) sensors. Event-based feature detection and tracking. Event-based motion estimation: optical flow estimation, 3D reconstruction, camera localization and ego-motion estimation, simultaneous localization and mapping (E-SLAM). Stereo depth estimation in dynamic scenes. Image intensity reconstruction from events. Event-based pattern recognition, classification and machine learning. Event-based signal processing and filtering. Event-based sensor fusion. Event-based control. Event-based (i.e., spike-based) hardware. Novel applications in event-based vision.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Event-based Robot VisionÜbung34342000L-002SoSeEnglish2
Event-based Robot VisionVorlesung34342000L-001SoSeEnglish4

Workload and Credit Points

Event-based Robot Vision (Übung):

Workload descriptionMultiplierHoursTotal
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h
90.0h(~3 LP)

Event-based Robot Vision (Vorlesung):

Workload descriptionMultiplierHoursTotal
Attendance15.04.0h60.0h
Pre/post processing15.02.0h30.0h
90.0h(~3 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The lectures will present the topics (sensors and algorithms) from a theoretical point of view, highlighting the underlying principles and mathematical tools used. Participation/interaction is encouraged and expected, including the possibility of reading assignments. Participants are expected to rehearse topics after class in preparation for the exercises. The exercises take place in parallel. They offer the participants the opportunity to get practical insights about the technology of event-based cameras. Simple event-based processing algorithms will be discussed in detail and partially implemented.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

The module covers many topics at the forefront of research. It requires a basic knowledge of information technology, image processing, computer vision and machine learning.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

NamePointsCategorieDuration/Extent
(Examination) Written test40written75 minutes
(Examination) Written test 230written75 minutes
(Deliverables) Practice exercises30practical2-4 pages, program code

Grading scale

Notenschlüssel »Notenschlüssel 1: Fak IV (1)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt86.0pt82.0pt78.0pt74.0pt70.0pt66.0pt62.0pt58.0pt54.0pt50.0pt

Test description (Module completion)

No information

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 30.

Registration Procedures

For any questions about the module, contact Prof. Gallego guillermo.gallego@tu-berlin.de

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
Gallego et al., Event-based Vision: A survey, 2019. https://arxiv.org/abs/1904.08405
Posch et al., Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output. Proc. IEEE (2014), 102(10):1470-1484
List of Event-based Vision Resources: https://github.com/uzh-rpg/event-based_vision_resources
CVPR 2019 Second International Workshop on Event-based Vision and Smart Cameras - Slides and Videos: http://rpg.ifi.uzh.ch/CVPR19_event_vision_workshop.html
ICRA 2017 First International Workshop on Event-based Vision - Slides and Videos: http://rpg.ifi.uzh.ch/ICRA17_event_vision_workshop.html

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)127SoSe 2020SoSe 2024
Computer Science (Informatik) (M. Sc.)127SoSe 2020SoSe 2024
Elektrotechnik (M. Sc.)118SoSe 2020SoSe 2024
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)19SoSe 2020SoSe 2024

Miscellaneous

Mastermodul: Kognitive Systeme und Informationssysteme