Introduction to 2D Computer Vision
This lecture overviews digital images and 2D Computer Vision (image analysis). Notable 2D Computer Vision topics are presented: edge detection, contour following, region segmentation, texture description, image topology, shape analysis, image registration, object detection.
Should you require access to the resource, please contact the author directly.
Computational Cinematography
This lecture overviews Computational Cinematography that has many applications in filming, notably in drone cinematography. It covers the following topics in detail: Framing Shot Types: eXtreme Close Up shot (XCU), Medium Shot (MS), Long Shot (LS), eXtreme Long Shot (XLS), Over The Shoulder (OTS) and their detection. Shot type constraints (notably on focal length due to target tracking and shot feasility are also presented.
Should you require access to the resource, please contact the author directly.
Object Pose Estimation
This lecture overviews object pose estimation that has many applications in Human-Robotic Interaction (HRI), Robotics and Augmented Reality. It covers the following topics in detail: definitions of body pose/posture, 6D object pose estimation through object detection, 3D object pose regression/classification/retrieval.
Should you require access to the resource, please contact the author directly.
3D Object Localization
This lecture overviews 3D Object Localization that has many applications in robotics and autonomous systems. It covers the following topics in detail: GPS object localization, Visual 3D object localization using 3D maps, Multisensor object localization, Multi-view object localization.
Should you require access to the resource, please contact the author directly.
Neural SLAM
This lecture overviews Neural SLAM that has many applications in robotic and autonomous vehicle localization and mapping. It covers the following topics in detail: Neural Camera Calibration, Neural Mapping/Reconstruction, Neural Localization, Neural Structure from Motion, Neural SLAM.
Should you require access to the resource, please contact the author directly.
Simultaneous Localization and Mapping
The lecture includes the essential knowledge about how we obtain/get 2D and/or 3D maps that robots/drones need, taking measurements that allow them to perceive their environment with appropriate sensors. Semantic mapping includes how to add semantic annotations to the maps such as POIs, roads and landing sites. Section Localization is exploited to find the 3D drone or target location based on sensors using specifically Simultaneous Localization and Mapping (SLAM). Finally, drone localization fusion describes improves accuracy on localization and mapping by exploiting the synergies between different sensors.
Should you require access to the resource, please contact the author directly.