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master's thesis
Uvjetna neuronska polja za semantičku estimaciju scene monokularnom kamerom
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:605089
Šestak, Josip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Šestak, J. (2024). Uvjetna neuronska polja za semantičku estimaciju scene monokularnom kamerom (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:605089

Šestak, Josip. "Uvjetna neuronska polja za semantičku estimaciju scene monokularnom kamerom." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:605089

Šestak, Josip. "Uvjetna neuronska polja za semantičku estimaciju scene monokularnom kamerom." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:605089

Šestak, J. (2024). 'Uvjetna neuronska polja za semantičku estimaciju scene monokularnom kamerom', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:605089

Šestak J. Uvjetna neuronska polja za semantičku estimaciju scene monokularnom kamerom [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:605089

J. Šestak, "Uvjetna neuronska polja za semantičku estimaciju scene monokularnom kamerom", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:605089

Metadata
TitleUvjetna neuronska polja za semantičku estimaciju scene monokularnom kamerom
Title (english)Conditional neural fields for monocular semantic scene estimation
AuthorJosip Šestak
MentorMarija Seder (mentor)
Committee memberIvan Petrović (član povjerenstva)
Committee memberLuka Petrović (član povjerenstva)
Committee memberIvan Marković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-09-17, Croatia
Abstract
Rad se bavi primjenom uvjetnih neuronskih polja za semantičku estimaciju scene koristeći monokularnu kameru. Cilj je razviti model koji može precizno klasificirati i segmentirati različite objekte unutar jedne slike snimljene monokularnom kamerom. Uvjetna neuronska polja kombiniraju prednosti konvolucijskih neuronskih mreža i uvjetnih slučajnih polja. To omogućuje modelu da uči složene prostorne odnose između piksela u slikama. Ključna komponenta pristupa je primjena samonadziranog učenja, metode u kojoj model koristi neoznačene podatke za generiranje vlastitih oznaka tijekom procesa obuke. Ovo smanjuje potrebu za velikim količinama ručno označenih podataka.
Abstract (english)
The work focuses on the application of conditional neural fields for semantic scene estimation using a monocular camera. The goal is to develop a model that can accurately classify and segment different objects within a single image captured by a monocular camera. Conditional neural fields combine the advantages of convolutional neural networks and conditional random fields. This allows the model to learn complex spatial relationships between pixels in images. A key component of the approach is the application of self-supervised learning, a method in which the model uses unlabeled data to generate its own labels during the training process. This reduces the need for large amounts of manually labeled data.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:605089
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2024-10-12 22:14:43