A description is given of a knowledge-based vision system for recognizing and classifying man-made objects in aerial images. Images are interpreted and image object descriptors are created, based on model-driven high-level vision processing. Knowledge of various low-level vision techniques, and their applicability to generic applications are used to dynamically select specific low-level vision techniques for image segmentation. Terrain map and feature information is used as an adjunct by the low-level vision to assist in image segmentation, and by high-level vision for image interpretation. The authors have used a unique software architecture based on standard knowledge-based approaches in which knowledge is represented explicitly and is separated from program control. Off-the-shelf tools including LISP and the ART language from Inference Corporation, running on a Symbolics 3675, were used. The current system has been tested both with low-resolution forward-looking infrared (FLIR) images for target cueing and higher-resolution airport scenes for scene analysis.


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    Title :

    A knowledge-based system for recognizing man-made objects in aerial images


    Additional title:

    Ein Wissens-basiertes System zur Erkennung von kuenstlichen Objekten in virtuellen Bildern


    Contributors:
    Smyrniotis, C. (author) / Dutta, K. (author)


    Publication date :

    1988


    Size :

    7 Seiten, 14 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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