Krzysztof Krawiec


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In this paper, we present a novel approach to genetic learning of high-level visual concepts that works with sets of attributed visual primitives rather than with raster images. The paper presents the approach in detail and verifies it in an experiment concerning locating objects in real-world 3D scenes.

@INPROCEEDINGS { Krawiec06a,
    AUTHOR = { Krzysztof Krawiec },
    TITLE = { Learning High-Level Visual Concepts Using Attributed Primitives and Genetic Programming },
    BOOKTITLE = { EvoWorkshops 2006 },
    YEAR = { 2006 },
    EDITOR = { F. R. },
    SERIES = { {LNCS} 3907 },
    PAGES = { 515--519 },
    ADDRESS = { Berlin Heidelberg },
    PUBLISHER = { Springer-Verlag },
    ABSTRACT = { In this paper, we present a novel approach to genetic learning of high-level visual concepts that works with sets of attributed visual primitives rather than with raster images. The paper presents the approach in detail and verifies it in an experiment concerning locating objects in real-world 3D scenes. },
    FILE = { http://www.cs.put.poznan.pl/kkrawiec/pubs/2006EVOIASPposter.pdf },
    URL = { http://dx.doi.org/10.1007/11732242_48 },
}


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