Usually, object detection is performed directly on (normalized) gray values or gray primitives like gradients or Haar-like features. In that case the learning of relationships between gray primitives, that describe the structure of the object, is the complete responsibility of the classifier. We propose to apply more knowledge about the image structure in the preprocessing step, by computing local isophote directions and curvatures, in order to supply the classifier with much more informative image structure features. However, a periodic feature space, like orientation, is unsuited for common classification methods. Therefore, we split orientation into two more suitable components. Experiments show that the isophote features result in better detection performance than intensities, gradients or Haar-like features.


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    Titel :

    Isophote properties as features for object detection


    Beteiligte:
    Lichtenauer, J. (Autor:in) / Hendriks, E. (Autor:in) / Reinders, M. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    611540 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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