376.054 Machine Vision and Cognitive Robotics
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2019W, VU, 4.0h, 6.0EC

Properties

  • Semester hours: 4.0
  • Credits: 6.0
  • Type: VU Lecture and Exercise

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, 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.

Teaching methods

  • 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_SIFT: Object recognition 2D: SIFT, SURF
  • Geometry_Stereo: geometry, basic calibration, stereo vision, 3D_Camera_Systems: Other methods to obtain 3D images
  • Attention_Ransac: 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

Mode of examination

Written and oral

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Wed14:00 - 17:0002.10.2019EI 2 Pichelmayer HS Einzeltermin _ Startvorlesung
Mon09:00 - 11:0007.10.2019 - 27.01.2020EI 2 Pichelmayer HS Lecture
Thu19:00 - 22:0021.11.2019EI 2 Pichelmayer HS Sondervortrag
Machine Vision and Cognitive Robotics - Single appointments
DayDateTimeLocationDescription
Wed02.10.201914:00 - 17:00EI 2 Pichelmayer HS Einzeltermin _ Startvorlesung
Mon07.10.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon14.10.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon21.10.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon28.10.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon04.11.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon11.11.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon18.11.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Thu21.11.201919:00 - 22:00EI 2 Pichelmayer HS Sondervortrag
Mon25.11.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon02.12.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon09.12.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon16.12.201909:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon13.01.202009:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon20.01.202009:00 - 11:00EI 2 Pichelmayer HS Lecture
Mon27.01.202009:00 - 11:00EI 2 Pichelmayer HS Lecture

Examination modalities

Positive of all exercises followed by oral examination. Weight for final grade: Labs:Lecture 60:40.

Course registration

Begin End Deregistration end
31.08.2019 00:00 21.10.2019 23:59 21.10.2019 23:59

Curricula

Literature

No lecture notes are available.

Previous knowledge

Matlab is mandatory; Background in Robotics is recommended, e.g., 376.040 Fachvertiefung Bildverarbeitung und Robotik. Python is helpful.

Continuative courses

Miscellaneous

Language

English