ADR Awareness Centre

This lecture overviews Pedestrian Detection  that has many applications in autonomous car vision and smart city applications.

This lecture overviews Road Traffic Monitoring that has many applications in in autonomous car perception and smart city management.

This lecture overviews 3D Object Localization that has many applications in robotics and autonomous systems.

This lecture overviews Kernel Methods that have many applications in classification and clustering. It covers the following topics in detail: Kernel Trick. Kernel Matrix. Kernel PCA. Kernel correlation and its use in object tracking. Kernel k-means.

This lecture overviews Active and Passive 3D shape reconstruction methods that has many applications in 3D computer vision, autonomous systems perception and medical imaging.

This lecture overviews Domain Adaptation that has many applications in DNN training and adaptation.

This lecture overviews Computational Aesthetics  that has many applications in visual arts and computer graphics. It covers the following topics in detail: Neuroaesthetics. Computational Aesthetics. Critical problems in aesthetics.

This lecture overviews various concepts related to multiple drone communications: LTE and WiFi communications, LTE communication infrastructure, IP network issues, Security analysis, throughput, latency and quality-of-service issues.

This lecture overviews Few Shot Object Recognition that has many applications in image classification, when few training data are available.  It covers the following topics in detail: Few-shot Image Learning definitions and methods.

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.

This book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), the task of training, by means of supervised learning, estimators of class proportions in unlabelled data.

A detailed introduction to computer vision will be made: image/video sampling, Image and video acquisition, Camera geometry, Stereo and Multiview imaging, Structure from motion, Structure from X, 3D Robot Localization and Mapping, Semantic 3D world m