dc.contributor.advisor |
Warglien, Massimo |
it_IT |
dc.contributor.author |
Dassiè, Federico <1997> |
it_IT |
dc.date.accessioned |
2022-10-26 |
it_IT |
dc.date.accessioned |
2023-02-22T11:18:38Z |
|
dc.date.available |
2023-02-22T11:18:38Z |
|
dc.date.issued |
2022-11-03 |
it_IT |
dc.identifier.uri |
http://hdl.handle.net/10579/22604 |
|
dc.description.abstract |
Saranno discussi i modi in cui il mondo del Machine Learning e della Computer Vision si stanno presentando al giorno d'oggi e di come, nel nostro caso, siano stati di fondamentale aiuto per migliorare l'esperienza lavorativa e soprattutto garantire una migliore efficienza tecnica. Saranno presentati numerosi esempi personali e dimostrazioni, basati su commissioni fatte e aspirazioni future. Nella fattispecie, la tesi si concentra sul lavoro svolto presso la Fondazione Giorgio Cini, che prevede una catena di passaggi automatici che parte dall'immagine grezza e arriva alla sua completa analisi finale, passando per una fase di post-produzione e la creazione e l'utilizzo di un modello classificatore. |
it_IT |
dc.language.iso |
en |
it_IT |
dc.publisher |
Università Ca' Foscari Venezia |
it_IT |
dc.rights |
© Federico Dassiè, 2022 |
it_IT |
dc.title |
Machine Learning and Computer Vision in the Humanities |
it_IT |
dc.title.alternative |
Machine Learning and Computer Vision in the Humanities |
it_IT |
dc.type |
Master's Degree Thesis |
it_IT |
dc.degree.name |
Digital and public humanities |
it_IT |
dc.degree.level |
Laurea magistrale |
it_IT |
dc.degree.grantor |
Dipartimento di Studi Umanistici |
it_IT |
dc.description.academicyear |
2021-2022_appello_171022 |
it_IT |
dc.rights.accessrights |
openAccess |
it_IT |
dc.thesis.matricno |
861867 |
it_IT |
dc.subject.miur |
INF/01 INFORMATICA |
it_IT |
dc.description.note |
We will discuss the ways in which the world of Machine Learning and Computer Vision are presenting themselves nowadays and how, in our case, they have been of fundamental help in improving the work experience and above all guaranteeing better technical efficiency. Preserving heritage, whether it will be a physical or digital aspect, is and always will be a difficult goal to reach. The main problem is given by the vastness of materials of different types, which make treatment difficult and lengthen the times. Since it is impossible or at least exhausting to carry out all the processing work by hand, image by image, it is necessary to be able to automate the process.
The thesis focuses on the work carried out at the Giorgio Cini Foundation, which includes a chain of automatic steps that starts from the raw image and arrives at its complete final analysis, passing through a post-production phase and the creation and use of an Object Detection model. The final result are the same starting images, now corrected according to different transformations and parameters, with a series of statistical information about their contents, both ready to be published for a broader public. |
it_IT |
dc.degree.discipline |
|
it_IT |
dc.contributor.co-advisor |
|
it_IT |
dc.date.embargoend |
|
it_IT |
dc.provenance.upload |
Federico Dassiè (861867@stud.unive.it), 2022-10-26 |
it_IT |
dc.provenance.plagiarycheck |
Massimo Warglien (warglien@unive.it), 2022-10-17 |
it_IT |