Making decisions based on a linear combination L of features is of course very common in pattern recognition. For distinguishing between two hypotheses or classes, the test is of the form sign (L - /spl tau/) for some threshold /spl tau/. Due mainly to fixing /spl tau/, such tests are sensitive to changes in illumination and other variations in imaging conditions. We propose a special case, a "self-normalized linear test" (SNLT), hard-wired to be of the form sign (L/sub 1/ - L/sub 2/) with unit weights. The basic idea is to "normalize" L/sub 1/, which involves the usual discriminating features, by L/sub 2/, which is composed of non-discriminating features. For a rich variety of features (e.g., based directly on intensity differences), SNLTs are largely invariant to illumination and robust to unexpected background variations. Experiments in face detection are promising: they confirm the expected invariances and out-perform some previous results in a hierarchical framework.


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    Titel :

    Self-normalized linear tests


    Beteiligte:
    Gangputra, S. (Autor:in) / Geman, D. (Autor:in)


    Erscheinungsdatum :

    01.01.2004


    Format / Umfang :

    576503 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Self-Normalized Linear Tests

    Gangaputra, S. / Geman, D. / IEEE Computer Society | British Library Conference Proceedings | 2004



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