Realtime detection and localization of a road from an aerial image is an emerging research area that can be applied to vision-based navigation of unmanned air vehicles. Existing realtime and non-realtime road detection algorithms focus on pre-defined road types, and a single algorithm cannot handle a large variety of road types such as dirt roads, local streets, and freeways. An algorithm to detecting any types of corridors is presented. First, a corridor structure is automatically learned at runtime with a single example. The corridor structure is represented as a cross-sectional 1-D signal segment. The learning procedure is to find the maximum correlation of such signals. The realtime detection consists of 1-D signal matching and robust fitting on the matching result. Realtime detection results on various road images are presented.


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    Titel :

    Realtime Road Detection by Learning from One Example


    Beteiligte:
    Kim, ZuWhan (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    918261 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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