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Generalization analysis of semantic segmentation with deep filter banks

Marko Prelevikj (2017) Generalization analysis of semantic segmentation with deep filter banks. EngD thesis.

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    Mobile robotic systems capable of autonomous navigation in non-structured environments depend on their vision module in order to safely navigate through the environment. The vision module provides perception of the surrounding area and it is often required to identify particular objects of interest, which is done by classifying image segments into pre-learned semantic classes. There are many methods which provide remarkable semantic segmentation results, but unfortunately only on specific datasets, which are not necessarily correlated to the scenes observed by a mobile robot. To verify the dataset's capability of transferring knowledge to a new domain we explore how well it generalises its classes. We examine the transfer of knowledge on a specific semantic segmentation method, which we adjust to best fit our needs.

    Item Type: Thesis (EngD thesis)
    Keywords: semantic segmentation, transfer of knowledge, convolutional neural networks, texture recognition
    Number of Pages: 94
    Language of Content: English
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Matej Kristan4053Mentor
    prof. dr. Vaclav HlavačComentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537459395)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3859
    Date Deposited: 27 Jun 2017 10:55
    Last Modified: 06 Jul 2017 11:03
    URI: http://eprints.fri.uni-lj.si/id/eprint/3859

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