376.081 Machine Vision
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2022W, VU, 3.0h, 4.5EC
TUWELLectureTube

Properties

  • Semester hours: 3.0
  • Credits: 4.5
  • Type: VU Lecture and Exercise
  • LectureTube course
  • Format: Presence

Learning outcomes

After successful completion of the course, students are able to solve first problems in the fields of in machine vision: basic computer vision methods, edge detection, region description, feature extraction, object tracking, depth image acquisition, methods of 2D and 3D object recognition, Deep learning and Machine Learning, Gestalt theory, depth image processing, cognitive vision; Focus in robotics on cognitive robots, situated vision for robotics, and robot systems.

Subject of course

Emphasis is on the following topics in machine vision: basic computer vision methods, edge detection, region description, feature extraction, object tracking, depth image acquisition, methods of 2D and 3D object recognition, Gestalt theory, depth image processing, cognitive vision; Focus in robotics on cognitive robots, situated vision for robotics, and robot systems.

  • Robots, robot tasks, cognitive robots, machine vision, vision applications; computer/machine/situated vision, and machine vision basics: camera, images, Filtering, SSD, Canny
  • Machine Vision Features: Industrial/mobile/cognitive robotics, sensors used in robotics;
  • Interest Points: Harris, DoG
  • Object recognition 2D: SIFT, SURF
  • Geometry Stereo: geometry, basic calibration, stereo vision 
  • 3D Camera Systems: Other methods to obtain 3D images
  • Attention
  • Ransac
  • 3D Vision Methods: voxel grids, neighbours, integral images, surface normal, differential geometry, Gestalt, Clustering
  • Object recognition in 3D: NARF, VFH, ESF, ..., examples, learning from CAD data
  • Deep learning: concept, introduction, applications, object categorisation
  • Open problems: human vision vs. robot vision, what works and open challenges

Teaching methods

The contents of the course are presented in the form of oral presentations supported by digital slides. To apply the theoretic knowledge, students have to implement 6 programming exercises about topics discussed during the lecture. The exercises have to be done using Python with the popular libraries OpenCV, Open3D and NumPy. The submission and grading of the exercises is done via TUWEL.

Introduction and first lecture: 4. October 14:00-17:00

Mode of examination

Written and oral

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Tue14:00 - 17:0004.10.2022EI 9 Hlawka HS - ETIT Introduction and First Lecture
Mon09:00 - 11:0010.10.2022 - 12.12.2022EI 4 Reithoffer HS Vorlesung
Machine Vision - Single appointments
DayDateTimeLocationDescription
Tue04.10.202214:00 - 17:00EI 9 Hlawka HS - ETIT Introduction and First Lecture
Mon10.10.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung
Mon17.10.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung
Mon24.10.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung
Mon31.10.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung
Mon07.11.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung
Mon14.11.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung
Mon21.11.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung
Mon28.11.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung
Mon05.12.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung
Mon12.12.202209:00 - 11:00EI 4 Reithoffer HS Vorlesung

Examination modalities

Positive of all exercises followed by oral examination. Weight for final grade: Exercises 60%, Oral Examination 40%.

Due to the current situation, exams are offered both in attendance and ALSO online with Zoom. Whether an appointment is an attendance exam or an online exam can be found in the information in TISS under the individual exam dates.

Examination candidates will be assigned a time slot of 30 minutes. This slot will be sent out by mail after the end of the registration deadline. A webcam is needed for proof of identity. The public link of the exam will be shared in TISS. Candidates with time constraints or restrictions in terms of equipment should contact us before the exam. 

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Fri09:00 - 12:0017.01.2025 Büro Prof. Vincze CB0509oral09.12.2024 00:00 - 15.01.2025 23:59TISSMachine Vision
Mon08:00 - 10:0027.01.2025 Office Prof. Vincze: CB0509oral16.12.2024 00:00 - 23.01.2025 23:55TISSMachine Vision
Mon14:00 - 16:0027.01.2025 Office Prof. Vincze: CB0509oral16.12.2024 00:00 - 23.01.2025 23:55TISSMachine Vision
Tue09:00 - 10:0028.01.2025 Office Prof. Vincze (CB0509)oral08.01.2025 10:00 - 27.01.2025 23:59TISSMachine Vision
Tue08:00 - 10:0004.02.2025 https://tuwien.zoom.us/j/68449203473?pwd=TWM1eG9vNFRnanlZbTN0NDBlamkyUT09oral08.01.2025 10:00 - 03.02.2025 23:59TISSMachine Vision - online
Tue11:00 - 14:0004.02.2025 https://tuwien.zoom.us/j/68449203473?pwd=TWM1eG9vNFRnanlZbTN0NDBlamkyUT09oral08.01.2025 10:00 - 03.02.2025 23:59TISSMachine Vision - online
Wed09:00 - 11:0005.02.2025 https://tuwien.zoom.us/j/68449203473?pwd=TWM1eG9vNFRnanlZbTN0NDBlamkyUT09oral03.02.2025 00:00 - 04.02.2025 23:59TISSMachine Vision - Online
Fri10:00 - 12:0007.03.2025 Office Prof. Vincze CB0509oral20.12.2024 00:00 - 05.03.2025 23:59TISSMachine Vision
Fri13:00 - 16:0007.03.2025 Office Prof. Vincze CB0509oral20.12.2024 00:00 - 05.03.2025 23:59TISSMachine Vision
Thu09:00 - 11:3017.04.2025 Office Markus Vincze CB0509oral19.02.2025 10:00 - 15.04.2025 08:00TISSMachine Vision
Fri09:00 - 11:0030.05.2025 Office Markus Vincze CB0509written17.04.2025 10:00 - 28.05.2025 10:00TISSMachine Vision

Course registration

Begin End Deregistration end
01.09.2022 00:00 17.10.2022 23:59

Curricula

Study CodeObligationSemesterPrecon.Info
066 504 Master programme Embedded Systems Mandatory elective
066 515 Automation and Robotic Systems Mandatory1. Semester
066 938 Computer Engineering Mandatory elective
066 938 Computer Engineering Mandatory elective

Literature

No lecture notes are available.

Previous knowledge

Knowledge of Python is recommended. Background in Robotics is helpful, e.g., 376.040 Fachvertiefung Bildverarbeitung und Robotik. 

Continuative courses

Language

English