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.

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_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

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

Due to the current situation, the oral exams will be conducted remotely using GoToMeeting until further notice.

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