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Indoor Scene Recognition based on Weighted Voting Schemes

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00334777" target="_blank" >RIV/68407700:21230/19:00334777 - isvavai.cz</a>

  • Nalezeny alternativní kódy

    RIV/68407700:21730/19:00334777

  • Výsledek na webu

    <a href="https://doi.org/10.1109/ECMR.2019.8870931" target="_blank" >https://doi.org/10.1109/ECMR.2019.8870931</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ECMR.2019.8870931" target="_blank" >10.1109/ECMR.2019.8870931</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Indoor Scene Recognition based on Weighted Voting Schemes

  • Popis výsledku v původním jazyce

    Scene understanding represents one of the most primary problems in computer vision. It implies the full knowledge of all the elements of the environment and the comprehension of the relationships between them. One of the major tasks in this process is the scene recognition, on which we focus in this work. Scene recognition is a relevant and helpful task in many robotic fields such as navigation, localization, manipulation, among others. The knowledge of the place (e.g. “office”, “classroom” or “kitchen”) can improve the performance of robots in indoor environments. This task can be difficult because of the variability, ambiguity, illumination changes, occlusions and scale variability present in this type of spaces. Commonly, this problem has been approached through the development of models based on local and global characteristics, incorporating context information and, more recently, using deep learning techniques. In this paper, we propose a multi-classifier model for scene recognition considering as priors the outcomes of independent base classifiers. We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance. The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem.

  • Název v anglickém jazyce

    Indoor Scene Recognition based on Weighted Voting Schemes

  • Popis výsledku anglicky

    Scene understanding represents one of the most primary problems in computer vision. It implies the full knowledge of all the elements of the environment and the comprehension of the relationships between them. One of the major tasks in this process is the scene recognition, on which we focus in this work. Scene recognition is a relevant and helpful task in many robotic fields such as navigation, localization, manipulation, among others. The knowledge of the place (e.g. “office”, “classroom” or “kitchen”) can improve the performance of robots in indoor environments. This task can be difficult because of the variability, ambiguity, illumination changes, occlusions and scale variability present in this type of spaces. Commonly, this problem has been approached through the development of models based on local and global characteristics, incorporating context information and, more recently, using deep learning techniques. In this paper, we propose a multi-classifier model for scene recognition considering as priors the outcomes of independent base classifiers. We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance. The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem.

Klasifikace

  • Druh

    D - Stať ve sborníku

  • CEP obor

  • OECD FORD obor

    20204 - Robotics and automatic control

Návaznosti výsledku

  • Projekt

    <a href="/cs/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotika pro Průmysl 4.0</a><br>

  • Návaznosti

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Ostatní

  • Rok uplatnění

    2019

  • Kód důvěrnosti údajů

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

Údaje specifické pro druh výsledku

  • Název statě ve sborníku

    Proceedings of European Conference on Mobile Robots

  • ISBN

    978-1-7281-3605-9

  • ISSN

  • e-ISSN

  • Počet stran výsledku

    6

  • Strana od-do

  • Název nakladatele

    Czech Technical University

  • Místo vydání

    Prague

  • Místo konání akce

    Prague

  • Datum konání akce

    4. 8. 2019

  • Typ akce podle státní příslušnosti

    WRD - Celosvětová akce

  • Kód UT WoS článku

    000558081900027