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master's thesis
Kartezijsko genetsko programiranje u konvoluciji slike
2021. urn:nbn:hr:168:627275
Šestić, Lukas
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Šestić, L. (2021). Kartezijsko genetsko programiranje u konvoluciji slike (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:627275

Šestić, Lukas. "Kartezijsko genetsko programiranje u konvoluciji slike." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:627275

Šestić, Lukas. "Kartezijsko genetsko programiranje u konvoluciji slike." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:627275

Šestić, L. (2021). 'Kartezijsko genetsko programiranje u konvoluciji slike', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 21 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:627275

Šestić L. Kartezijsko genetsko programiranje u konvoluciji slike [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 July 21] Available at: https://urn.nsk.hr/urn:nbn:hr:168:627275

L. Šestić, "Kartezijsko genetsko programiranje u konvoluciji slike", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:627275

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Metadata
TitleKartezijsko genetsko programiranje u konvoluciji slike
Title (english)Cartesian Genetic Programming for image convolution
AuthorLukas Šestić
MentorDomagoj Jakobović (mentor)
Committee memberDomagoj Jakobović (predsjednik povjerenstva)
Committee memberMarko Đurasević (član povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad bavi se obradom slike konvolucijskim metodama koristeći Kartezijsko genetsko programiranje (CGP). Istražuju se mogućnosti i performanse razvoja filtera za obradu slike. Prikazuju se rezultati pri micanju šuma i detekciji rubova, pokazujući prednosti koje dolaze s korištenjem CGP-a. Nažalost, pokušaji generiranja podataka izvedbom autoenkodera CGP-om ne rezultiraju uspjehom. Do izražaja dolaze određeni nedostatci genetskog programiranja koji ukazuju na korištenje prikladnijih tehnika za određene probleme.
Abstract (english)
This paper describes the process of image convolution using Cartesian genetic programming (CGP). Possibilities and performance in evolving image filters are explored. Results are shown with image denoising filters and edge detection, pointing to the benefits of using CGP. Unfortunately, synthetic data generation using CGP as an autoencoder does not result in such success. Various downsides of genetic programming make other, more appropriate deep learning techniques a better choice for some problems.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:627275
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 16:15:47