Ralph Versteegen

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We introduce a new simple framework for texture modelling with Markov--Gibbs random fields (MGRF). The framework learns texture-specific high order pixel interactions described by feature functions of signal patterns. Currently, modelling of high order interactions is almost exclusively achieved by linear filtering. Instead we investigate `binary pattern'(More)
Currently, Markov–Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure by considering the learning process in(More)
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