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undergraduate thesis
Usporedba algoritama čvrstog i neizrazitog grupiranja podataka za segmentaciju slike
2018. urn:nbn:hr:200:188009
Loina, Luka
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Programming Languages and Systems

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Loina, L. (2018). Usporedba algoritama čvrstog i neizrazitog grupiranja podataka za segmentaciju slike (Undergraduate 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:200:188009

Loina, Luka. "Usporedba algoritama čvrstog i neizrazitog grupiranja podataka za segmentaciju slike." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2018. https://urn.nsk.hr/urn:nbn:hr:200:188009

Loina, Luka. "Usporedba algoritama čvrstog i neizrazitog grupiranja podataka za segmentaciju slike." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2018. https://urn.nsk.hr/urn:nbn:hr:200:188009

Loina, L. (2018). 'Usporedba algoritama čvrstog i neizrazitog grupiranja podataka za segmentaciju slike', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 20 November 2024, https://urn.nsk.hr/urn:nbn:hr:200:188009

Loina L. Usporedba algoritama čvrstog i neizrazitog grupiranja podataka za segmentaciju slike [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2018 [cited 2024 November 20] Available at: https://urn.nsk.hr/urn:nbn:hr:200:188009

L. Loina, "Usporedba algoritama čvrstog i neizrazitog grupiranja podataka za segmentaciju slike", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:200:188009

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Metadata
TitleUsporedba algoritama čvrstog i neizrazitog grupiranja podataka za segmentaciju slike
Title (english)A comparison of crisp and fuzzy data clustering algorithms for image segmentation
AuthorLuka Loina
MentorGoran Martinović (mentor)
MentorDražen Bajer (mentor)
Committee memberGoran Martinović (predsjednik povjerenstva)
Committee memberZdravko Krpić (član povjerenstva)
Committee memberRobert Cupec (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Programming Languages and Systems)
Osijek
Defense date and country2018-11-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
U ovom radu napravljena je usporedba algoritama za grupiranje podataka primijenjenih na segmentaciju slike. Rad ukratko opisuje probleme grupiranja podataka i segmentacije slike. Posebna pažnja posvećena je algoritmima za grupiranje podataka k-means i fuzzy c-means i njihovoj primjeni za segmentaciju slike. Za potrebe rada napravljeno je programsko rješenje pomoću kojega se slike mogu segmentirati korištenjem algoritma k-means ili fuzzy c-means. Programsko rješenje izraĎeno je u programskom jeziku C# uz korištenje radnih okvira Microsoft .NET i Windows forme. Usporedba algoritama provedena je na tri standardne slike. Svaka slika segmentirana je algoritmima k-means i fuzzy c-means sa eksponentima težine 1.1, 2 i 5. Dobiveni rezultati vrednovani su vizualnom inspekcijom slika i usporedbom vremena izvoĎenja algoritama.
Abstract (english)
A comparison of crisp and fuzzy data clustering algorithms for image segmentation This paper provides comparison of clustering based image segmentation methods. Paper gives short introduction to image segmentation and clustering. Greater attention was given to k-means and fuzzy c-means clustering algorithms and their application in image segmentation. Software for image segmentation by using k-means or fuzzy c-means algorithm was implemented as part of writing this paper. Software was implemented in programming language C# with Microsoft .NET and Windows forms frameworks. Three standard images were used for comparison of image segmentation algorithms. Each image was segmented with k-means and fuzzy c-means with fuzzifier value of 1.1, 2 and 5. Results of segmentations were evaluated by visual inspection and comparison of execution times.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:188009
Study programmeTitle: Undergraduate University Study Programme in Computer Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2018-11-26 11:45:50