Thesis Details
Fast Analysis of Borders in Image
This thesis focuses on the problem of detecting edges in natural images while maintaining high performance per image. First, the existing approaches are analysed and from them the relevant information is extracted. This information is then used to design two architectures that use convolutional neural networks. One architecture is based on RCF and enriches the output, while the other is a combination of RCF and RCN. This combination provides better up-sampling and enriches the output even more. Evaluation was performed on the BSDS500 dataset and the best result was for achieved for the model that combined RCF and RCN with an ODS score of 0.675.
neural network, machine learning, convolution neural network, edge detection
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Burget Radek, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT19586, author = "Matej Koles\'{a}r", type = "Bachelor's thesis", title = "Fast Analysis of Borders in Image", school = "Brno University of Technology, Faculty of Information Technology", year = 2020, location = "Brno, CZ", language = "english", url = "https://www.fit.vut.cz/study/thesis/19586/" }