Unlock the world of Geometric Learning and Graph Convolutional Networks (GCNs) in this comprehensive course designed to empower you with cutting-edge knowledge and practical skills. Geometric learning is a fascinating field at the intersection of computer vision, machine learning, and data analysis, with applications ranging from image processing to 3D modeling and beyond.
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Domain Adaptation and Generalization
There is an issue of domain shift in machine learning models, which occurs when models trained on one dataset perform poorly when tested on data from a different source. This problem is particularly relevant for visual models operating in the real world or when no labelled data is available for the target scenario. Domain adaptation… Continue reading Domain Adaptation and Generalization
Drone Vision for Infrastructure Inspection
This lecture overviews the use of drones for infrastructure inspection and maintenance. Various types of inspection, e.g., using visual cameras, LIDAR or thermal cameras are reviewed. Primary application area is electric line inspection. Line detection and tracking and drone perching are examined. Human action recognition and co-working assistance are overviewed.
Deep Video Summarization
Nowadays, both social media sites, Media Archiving and Management (MAM) systems, and broadcaster mediatheques store huge amounts of video information. It search can be very tedious, whether for journalistic purposes or just for selecting a video to watch and entertain. Therefore, fast video browsing is an essentialservice to be provided in a www environment. One… Continue reading Deep Video Summarization
Image Features
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Region Segmentation
This lecture overviews Region Segmentation and Texture Description that have many applications in in image analysis, notably in shape description and object recognition It covers the following topics in detail: Image segmentation by thresholding. Region growing techniques. Split techniques and Quadtrees. Split /merge techniques. Relaxation algorithms in region analysis. Neural region segmentation.