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)
Descriptive abilities of translation-invariant Markov-Gibbs random fields (MGRF), common in texture modelling, are expected to increase if higher-order interactions, i.e. conditional dependencies between larger numbers of pixels, are taken into account. But the complexity of modelling grows as well, so that most of the recent high-order MGRFs are built to a(More)
Markov random field models of textures describe images by statistics of local image features and identify conditional dependencies between pixels. To account for higher-order inter-dependencies between multiple pixels, most common high-order modelling follows the well-known FRAME or Fields of Experts (FoE) frameworks in using marginals of the responses of(More)
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