Tesi etd-05012020-101143 |
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Tipo di tesi
Tesi di dottorato di ricerca
Autore
PAVONI, GAIA
URN
etd-05012020-101143
Titolo
AUTOMATIZING THE LARGE-SCALE ANALYSIS OF UNDERWATER OPTICAL DATA
Settore scientifico disciplinare
ING-INF/04
Corso di studi
INGEGNERIA DELL'INFORMAZIONE
Relatori
tutor Prof. Pollini, Lorenzo
relatore Prof. Caiti, Andrea
relatore Dott. Corsini, Massimiliano
relatore Prof. Caiti, Andrea
relatore Dott. Corsini, Massimiliano
Parole chiave
- Intelligent tools
- human-in-the -loop
- Automatic recognition
Data inizio appello
04/05/2020
Consultabilità
Non consultabile
Data di rilascio
04/05/2023
Riassunto
Underwater monitoring provides essential information to analyze the current condition and persisting trends of marine habitats. The optical data acquisition is a powerful solution to ensure both high-resolution and large-scale sampling of the seafloor. The use of autonomous data-driven robotics is making underwater imaging more and more popular. Nevertheless, video and image sequences are a trustworthy source of knowledge that remains partially unexploited: the human visual analysis of images is a very time-consuming task, which creates a bottleneck between data collection and extrapolation.
This thesis presents a human-in-the-loop software solution, based on deep learning methodologies and computer vision, suitable for supporting and speeding up the analysis of visual data coming from underwater environmental monitoring activities.
This thesis presents a human-in-the-loop software solution, based on deep learning methodologies and computer vision, suitable for supporting and speeding up the analysis of visual data coming from underwater environmental monitoring activities.
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