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master's thesis
Semantička segmentacija primjeraka korištenjem dubokih konvolucijskih modela
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:575012
Balun, Matej
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Balun, M. (2021). Semantička segmentacija primjeraka korištenjem dubokih konvolucijskih modela (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:575012

Balun, Matej. "Semantička segmentacija primjeraka korištenjem dubokih konvolucijskih modela." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:575012

Balun, Matej. "Semantička segmentacija primjeraka korištenjem dubokih konvolucijskih modela." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:575012

Balun, M. (2021). 'Semantička segmentacija primjeraka korištenjem dubokih konvolucijskih modela', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:575012

Balun M. Semantička segmentacija primjeraka korištenjem dubokih konvolucijskih modela [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:575012

M. Balun, "Semantička segmentacija primjeraka korištenjem dubokih konvolucijskih modela", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:575012

Metadata
TitleSemantička segmentacija primjeraka korištenjem dubokih konvolucijskih modela
Title (english)Semantic Segmentation of Instances Using Deep Convolutional Models
AuthorMatej Balun
MentorTomislav Hrkać (mentor)
Committee memberTomislav Hrkać (predsjednik povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
Committee memberŽeljka Mihajlović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-02-19, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Segmentacija instanci problem je suvremenog računalnog vida i specifično domene dubokog učenja. Sama problematika kombinacije je klasifikacije, semantičke segmentacije, detekcije i lokalizacije objekata, dajući tako kompletnu informaciju o klasama objekata i pojedinim instancama tih klasa, u obliku okvira, koordinata, segmentnih maski i labela klasa i njihovih instanci. Facebook AI research team (FAIR) u članku iz 2017. godine predlaže model za segmentaciju instanci pod imenom Mask R-CNN. Model je baziran na prijašnjim modelima za zadaću detekcije objekata poput R-CNN, Fast R-CNN, Faster R-CNN itd. U ovome radu proučavana je implementacija i performanse modela na skupu uličnih urbanih prizora pod imenom Cityscapes, u kojem model daje daleko bolje rezultate nego ostali pristupi u problemu segmentacije instanci na tom skupu, ali i otvara mjesta za daljnja poboljšanja i nove te učinkovitije pristupe.
Abstract (english)
Instance segmentation is a problem of modern computer vision and specifically the domain of deep learning. The problem is a combination of classification, semantic segmentation, detection, and localization of objects, thus giving complete information about object classes and individual instances of those classes, in the form of frames, coordinates, segment masks, and labels of classes and their instances. Facebook AI research team (FAIR) in a 2017 article proposes a model for instance segmentation called Mask R-CNN. The model is based on previous models for object detection tasks such as R-CNN, Fast R-CNN, Faster R-CNN, etc. This paper studies the implementation and performance of the model on a set of street urban scenes called Cityscapes, in which the model gives far better results than other approaches to the problem of instance segmentation in that set, but also opens room for further improvements and new and more efficient approaches.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:575012
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
Access conditionsClosed access
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Created on2023-01-13 09:43:21