01022nam0 2200313 450 00002390020090304142003.088-15-03239-820090304d1991----km-y0itay50------baitaITy-------001yyOccupazione, distribuzione e crescitaJoan V. RobinsonBolognaIl Mulino1991LXV, 447 p.22 cmCollezione di testi e di studiEconomiaTrad. e ed. italiana a cura di Maria Cristina MarcuzzoRaccolta di saggi2001Collezione di testi e di studiOccupazione, distribuzione e crescita44795EconomiaTeorieSec. 20330.1511Robinson,Joan32461Marcuzzo,Maria CristinaITUNIPARTHENOPE20090304RICAUNIMARC000023900023/2247112NAVA22009Occupazione, distribuzione e crescita44795UNIPARTHENOPE01926nam 2200409z- 450 991080567690332120260219090038.0(CKB)5720000000263578(oapen)doab91407(IL-JeEL)995720000000263578(EXLCZ)99572000000026357820240213c2022uuuu -u- |gerurmn|---annantxtrdacontentcrdamediacrrdacarrierKonvolutionäre neuronale Netze in der industriellen Bildverarbeitung und Robotik /Norbert MitschkeKarlsruhe:KIT Scientific Publishing,2022.1 online resource (212 p.)Forschungsberichte aus der Industriellen Informationstechnik;269783731511977 3-7315-1197-5 Includes bibliographical references and index.In the first part of this dissertation, a framework for the design of a CNN for FPGAs is presented, consisting of a preprocessing algorithm, an augmentation technique, a custom quantization scheme and a pruning step of the CNN. The combination of conventional image processing with neural networks is shown in the second part by an example from robotics, where an image-based visual servoing process is successfully conducted for a gripping process of a robot.Image processingIndustrial applicationsRoboticsIndustrial applicationsMachine learningIndustrial applicationsImage processingIndustrial applications.RoboticsIndustrial applications.Machine learningIndustrial applications.621.399Mitschke Norbert1279000BOOK9910805676903321Konvolutionäre neuronale Netze in der industriellen Bildverarbeitung und Robotik3014368UNINA