Alberto Signoroni

TL;DR: The present review is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields and the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectrals data from a multidisciplinary perspective.

TL;DR: The adopted deep learning approach outperformed the handcrafted feature based one, and also a conventional reference technique, by a large margin, becoming a preferable solution for the addressed Digital Microbiology Imaging quantification task, especially in the emerging context of Full Laboratory Automation systems.

TL;DR: The current state-of-the-art in SVC is described, focusing on wavelet based motion-compensated approaches (WSVC), and individual components that have been designed to address the problem over the years are reviewed and how such components are typically combined to achieve meaningful WSVC architectures are reviewed.

TL;DR: This paper designs and test the essential steps of the entire production process of the customized production of hand orthosis with particular emphasis on the accurate acquisition of the forearm geometry and on the subsequent production of a printable model of the orthosis.

TL;DR: In this paper, an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients was proposed.