Recognizing and classifying all the entities appearing in an image is a fundamental goal of computer vision, and constitutes a key element in the semantic understanding of images, videos and other multi-media resources. This macro-activity is concerned with the development of deep learning theory and practice to understand the image content. More images depicting the same objects can be used for a 3D reconstruction of the scene.
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Non-invasive technologies for monitoring and understanding complex environments have applications in diverse domains such as security in industrial environment, outdoor and indoor surveillance, person action recognition, traffic analysis, assisted living, customer behaviour, sports analysis etc. This macro-activity is concerned with the development of computer vision technologies for dynamic scene understanding in such applicative contexts. The most recently used techniques are based on machine learning and deep learning with structured output prediction.
Augmented Reality (AR) seeks to enrich the users view of the environment with relevant information. It has raised importance with the pervasive use of mobile appliances such as smart-phones: in this case our activities focus on low-complexity computer vision methods for analyzing images captured with mobile devices to aid AR services such as geo-referenced information overlay and indoor navigation. AR is making more and more impact on Industry 4.0.