Design and management of image processing pipelines within CPS: acquired experience towards the end of the FitOptiVis ECSEL Project Sau, Carlo Rinaldi, Claudia Pomante, Luigi Palumbo, Francesca Valente, Giacomo Fanni, Tiziana Martínez, Marcos Van der Linden, Frank Basten, Twan Geilen, Marc Peeren, Geran Kadlec, Jiri Jääskeläinen, Pekka Bulej, Lubomir Barranco Expósito, Francisco Saarinen, Jukka Säntti, Tero Zedda, Maria Katiuscia Sánchez, Víctor Nikkhah, Shayan Tabatabaei Goswami, Dip Amat, Guillermo Maršík, Lukáš Van Helvoort, Mark Medina, Luis Al-Ars, Zaid De Beer, Ad Image processing Video processing Distributed system Heterogeneous system Multi-objective optimization Cyber-Physical System Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints. 2024-10-28T09:29:46Z 2024-10-28T09:29:46Z 2021 journal article Published version: Sau, Carlo. Design and management of image processing pipelines within CPS: acquired experience towards the end of the FitOptiVis ECSEL Project. Microprocessors and Microsystems Volume 87, November 2021, 104350. https://doi.org/10.1016/j.micpro.2021.104350 https://hdl.handle.net/10481/96381 10.1016/j.micpro.2021.104350 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier