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Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932070" target="_blank" >RIV/49777513:23520/17:43932070 - isvavai.cz</a>

  • Result on the web

    <a href="http://ieeexplore.ieee.org/document/7966418/" target="_blank" >http://ieeexplore.ieee.org/document/7966418/</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/IJCNN.2017.7966418" target="_blank" >10.1109/IJCNN.2017.7966418</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection

  • Original language description

    Horizon or skyline detection plays a vital role towards mountainous visual geo-localization, however most of the recently proposed visual geo-localization approaches rely on user-in-the-loop skyline detection methods. Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. This paper provides a quantitative comparison of four such methods for autonomous horizon/sky line detection on an extensive data set. Specifically, we provide the comparison between four recently proposed segmentation methods; one explicitly targeting the problem of horizon detection[2], second focused on visual geolocalization but relying on accurate detection of skyline [15] nd other two proposed for general semantic segmentation – Fully Convolutional Networks (FCN) [21] and SegNet[22]. Each of the first two methods is trained on a common training set [11] comprised of about 200 images while models for the third and fourth method are fine tuned for sky segmentation problem through transfer learning using the same data set. Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions. We report average accuracy and average absolute pixel error for each of the presented formulation.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

    <a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Neural Networks (IJCNN), 2017 International Joint Conference on

  • ISBN

    978-1-5090-6182-2

  • ISSN

    2161-4393

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    4436-4443

  • Publisher name

    IEEE

  • Place of publication

    New York

  • Event location

    Anchorage, Alaska, USA

  • Event date

    May 14, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000426968704091