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master's thesis
Segmentacija slike temeljena na algoritmu šišmiša
2024. urn:nbn:hr:200:302142
Antunović, Dražen
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Visual Computing

Cite this document

Antunović, D. (2024). Segmentacija slike temeljena na algoritmu šišmiša (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:302142

Antunović, Dražen. "Segmentacija slike temeljena na algoritmu šišmiša." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:200:302142

Antunović, Dražen. "Segmentacija slike temeljena na algoritmu šišmiša." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:200:302142

Antunović, D. (2024). 'Segmentacija slike temeljena na algoritmu šišmiša', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 22 January 2025, https://urn.nsk.hr/urn:nbn:hr:200:302142

Antunović D. Segmentacija slike temeljena na algoritmu šišmiša [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2024 [cited 2025 January 22] Available at: https://urn.nsk.hr/urn:nbn:hr:200:302142

D. Antunović, "Segmentacija slike temeljena na algoritmu šišmiša", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:200:302142

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Metadata
TitleSegmentacija slike temeljena na algoritmu šišmiša
Title (english)Image segmentation based on bat algorithm
AuthorDražen Antunović
MentorČaslav Livada (mentor)
Committee memberČaslav Livada (predsjednik povjerenstva)
Committee memberAlfonzo Baumgartner (član povjerenstva)
Committee memberTomislav Galba (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Visual Computing)
Osijek
Defense date and country2024-09-19, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Data Processing
Abstract
U ovom radu prvo je objašnjen koncept algoritma šišmiša kao i način njegova rada. Zatim su objašnjene vrste i metode segmentacije slike. Potom slijedi opis različitih varijanti algoritma šišmiša koje su korištene u svrhu segmentacije slika kao i različite metode koje su korištene sa tim varijantama te njihov međusobni rad zajedno. Kod varijanti algoritma šišmiša pojavljuju se pojmovi poput kaosa, inercijskih težina, dok je kod metoda objašnjena Otsuova metoda, K-means algoritam te rad sa neuronskim mrežama. Kod rada sa neuronskim mrežama pojašnjeni su pojedini pojmovi iz dubokog učenja poput: stohastičkog gradijentnog spuštanja sa momentom, gubitak unakrsne entropije, srednji IoU itd. Poslije su izvršena različita mjerenja za pojedine varijante algoritma šišmiša ovisno o tipu segmentacije. Na kraju su prikazani i raspravljeni rezultati.
Abstract (english)
This paper firstly explains the concept of the bat algorithm and its working mechanism. After that types and methods of image segmentation are explained. Following this, different variants of bat algorithm are described, along with different methods employed with these variants and their functionality altogether. These variants include terms such as chaos and inertia weights, while the methods involve explanation of Otsu's method, K-means algorithm and working with neural networks. When working with neural networks some of the terms from deep learning are clarified including: stochastic gradient descent with momentum, cross-entropy loss, mean IoU and so on. Later, different types of measurements are conducted for specific variants depending on the type of segmentation. In the end results are presented and discussed.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:302142
Study programmeTitle: Graduate University Study Programme in Computer Engineering
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni/a magistar/magistra inženjer/inženjerka računarstva (sveučilišni/a magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-10-08 06:09:50