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Published Articles >> Table of Contents >> Abstract
2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 2
pp. 616-622
Self-Normalized Linear Tests
Sachin Gangaputra, Johns Hopkins University
Donald Geman, Johns Hopkins University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2004.225
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| Abstract |
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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 - τ ) for some threshold τ. Due mainly to fixing τ , 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_1 - L_2) with unit weights. The basic idea is to "normalize" L_1, which involves the usual discriminating features, by L_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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Citation:
Sachin Gangaputra, Donald Geman,
"Self-Normalized Linear Tests,"
cvpr,
pp. 616-622,
2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 2,
2004
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