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master's thesis
Slabo nadzirana semantička segmentacija rukom pisanih jednadžbi
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:902575
Milić, Josip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Milić, J. (2017). Slabo nadzirana semantička segmentacija rukom pisanih jednadžbi (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:902575

Milić, Josip. "Slabo nadzirana semantička segmentacija rukom pisanih jednadžbi." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:902575

Milić, Josip. "Slabo nadzirana semantička segmentacija rukom pisanih jednadžbi." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:902575

Milić, J. (2017). 'Slabo nadzirana semantička segmentacija rukom pisanih jednadžbi', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:902575

Milić J. Slabo nadzirana semantička segmentacija rukom pisanih jednadžbi [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:902575

J. Milić, "Slabo nadzirana semantička segmentacija rukom pisanih jednadžbi", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:902575

Metadata
TitleSlabo nadzirana semantička segmentacija rukom pisanih jednadžbi
Title (english)Weakly-supervised semantic segmentation of hand-written equations
AuthorJosip Milić
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberSlobodan Ribarić (član povjerenstva)
Committee memberJan Šnajder (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-07-17, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Semantička segmentacija je važan zadatak računalnog vida. Cilj tog zadatka je odrediti semantički razred svakog pojedinog piksela slike strojno naučenim modelom. Kao i u drugim problemima računalnog vida, najbolji rezultati postižu se strogo nadziranim učenjem. Međutim, veliki nedostatak tog pristupa je potreba za ručnim označavanjem svakog pojedinog piksela u skupu slika za učenje. Kako bismo smanjili potrebu za tim skupim procesom, razmatramo mogućnost slabo nadziranog učenja segmentacijskih modela. U ovom pristupu na temelju anotacije na razini okvira u skupu podataka za učenje dobiva se anotacija na razini piksela znakova rukom pisanih jednadžbi u skupu podataka za ispitivanje.
Abstract (english)
Semantic segmentation is an important task of computer vision. The goal of this task is to determine the semantic class of each individual pixel in a machine-learned model. As in other computer vision problems, the best results are achieved by strictly controlled learning. However, the major disadvantage of this approach is the need to manually mark each pixel in a learning image set. To reduce the need for this expensive process, we consider the possibility of poorly supervised learning of segmentation models. In this approach, based on the bounding box level annotation in the train dataset, annotation is obtained at the level of character pixels of handwritten equations in the test dataset.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:902575
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 on2019-03-13 23:09:34