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- W2096009969 abstract "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 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." @default.
- W2096009969 created "2016-06-24" @default.
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- W2096009969 date "2004-11-13" @default.
- W2096009969 modified "2023-09-27" @default.
- W2096009969 title "Self-normalized linear tests" @default.
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- W2096009969 doi "https://doi.org/10.1109/cvpr.2004.1315221" @default.
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