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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    Title :

    Realtime Road Detection by Learning from One Example


    Contributors:
    Kim, ZuWhan (author)


    Publication date :

    2005-01-01


    Size :

    918261 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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