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Local ordinal signal relations, such as local binary or ternary patterns (LBP/LTPs) are invariant to frequent in practice spatially variant contrast/offset deviations that preserve image appearance. Our prior work extended this conventional LBP/LTP-based classifiers towards learning, rather than pre-scribing characteristic shapes, sizes, and numbers of such patterns. The learned LTPs showed more accurate...
A conventional framework for learning generic translation-invariant 2nd-order Markov-Gibbs random field (MGRF) models of spatially homogeneous textures is extended onto higher-order ones, which are also invariant to arbitrary perceptive (contrast-offset) signal deviations. Given a training image, the framework estimates both the geometry and strengths (potentials) of multiple conditional signal dependencies,...
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