Download
PDF 132.78 KB
undergraduate thesis
23   10
Comparing feature extraction methods for texture classification
Josip Baketarić (2015)
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
Cite this document...

Baketarić, J. (2015). Usporedba metoda za dohvaćanje značajki pri klasifikaciji tekstura (Undergraduate thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:246946

Baketarić, Josip. "Usporedba metoda za dohvaćanje značajki pri klasifikaciji tekstura." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2015. https://urn.nsk.hr/urn:nbn:hr:200:246946

Baketarić, Josip. "Usporedba metoda za dohvaćanje značajki pri klasifikaciji tekstura." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2015. https://urn.nsk.hr/urn:nbn:hr:200:246946

Baketarić, J. (2015). 'Usporedba metoda za dohvaćanje značajki pri klasifikaciji tekstura', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 20 February 2019, https://urn.nsk.hr/urn:nbn:hr:200:246946

Baketarić J. Usporedba metoda za dohvaćanje značajki pri klasifikaciji tekstura [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2015 [cited 2019 February 20] Available at: https://urn.nsk.hr/urn:nbn:hr:200:246946

J. Baketarić, "Usporedba metoda za dohvaćanje značajki pri klasifikaciji tekstura", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:200:246946

Metadata
TitleUsporedba metoda za dohvaćanje značajki pri klasifikaciji tekstura
AuthorJosip Baketarić
Mentor(s)Goran Martinović (thesis advisor)
Bruno Zorić (thesis advisor)
Abstract
U radu su analizirane tri različite metode za dohvaćanje značajki pri klasifikaciji tekstura. Korištene metode su histogram, LBP te Haarova valić metoda. U teorijskom dijelu objašnjene su metode za dohvaćanje značajki, neki klasifikatori, metode za ocjenjivanje klasifikatora i načini implementacije metoda. Praktični dio sastoji se od implementacije navedenih metoda u programskom jeziku C# te od analize rezultata u alatu za rudarenje podatcima Weka. Ova tema postaje sve više popularna u svijetu jer je doba digitalizacije i računala te se sve pokušava svesti na to da računalo što više posla obavi umjesto ljudi kako bi se uštedjelo što više vremena, a time i novca. Prema dobivenim rezultatima najbolja metoda, na primjenjenim teksturama, je histogram.
KeywordsHaar wavelet histogram LBP textures Weka.
Parallel title (English)Comparing feature extraction methods for texture classification
Committee MembersGoran Martinović (committee member)
Hrvoje Glavaš (committee member)
Irena Galić (committee member)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Lower level organizational unitsDepartment of Software Engineering
Chair of Programming Languages and Systems
PlaceOsijek
StateCroatia
Scientific field, discipline, subdisciplineTECHNICAL SCIENCES
Computing
Program Engineering
Study programme typeuniversity
Study levelundergraduate
Study programmeUndergraduate University Study Programme in Computer Engineering
Academic title abbreviationuniv.bacc.ing.comp.
Genreundergraduate thesis
Language Croatian
Defense date2015-07-09
Parallel abstract (English)
This paper analyses three different methods for extraction features during texture classification. The utilized methods are: histogram, LBP and the Haar wavelet method. The theoretical part of the paper elaborates on the methods for extraction features, some of the classifiers, methods for evaluating classifiers as well as on the ways of implementing the methods. The practical part of the paper is comprised of two sections: the implementation of the previously mentioned methods in a programming language C#, and the analysis of the results in a data mining tool Weka. This topic is constantly gaining popularity nowadays due to the digital era in which the computer is doing most of the work, so people can save time, as well as money.
Parallel keywords (Croatian)Haarov valić histogram LBP teksture Weka.
Resource typetext
Access conditionOpen access
Terms of usehttp://rightsstatements.org/vocab/InC/1.0/
URN:NBNhttps://urn.nsk.hr/urn:nbn:hr:200:246946