1 Einleitung

We address the problem of extending the field of view of a photo— an operation we call uncrop. Given a reference photograph to be uncropped, our approach selects, reprojects, and composites a subset of Internet imagery taken near the reference into a larger image around the reference using the underlying scene geometry. The proposed Markov Random Field based approach is capable of handling larg...

We present a novel approach for contextual classification of image patches in complex visual scenes, based on the use of histograms of quantized features and probabilistic aspect models. Our approach uses context in two ways: (1) by using the fact that specific learned aspects correlate with the semantic classes, which resolves some cases of visual polysemy often present in patch-based represen...

ion of Thesis Covering Trees: New Variational Bounds for MAP Estimation in Markov

The paper proposes a method for constructing an informative neighborhood for modeling texture images. To describe the characteristic features of textures used assumptions underlying model representation texture images described by using a Markov random field. The results of the conducted experimental researches confirm that application of the developed approach allows to reduce the dimensionali...

We consider the Merton problem of optimal portfolio choice when the traded instruments are the set of zero-coupon bonds. Working within a Markovian Heath–Jarrow–Morton model of the interest rate term structure driven by an infinite-dimensional Wiener process, we give sufficient conditions for the existence and uniqueness of an optimal trading strategy. When there is uniqueness, we provide a cha...

We introduce an empirical Bayesian procedure for the simultaneous segmentation of an observed motion field and estimation of the hyper-parameters of a Markov random field prior. The new approach approach exhibits the Bayesian appeal of incorporating prior beliefs, but requires only a qualitative description of the prior, avoiding the requirement for a quantitative specification of its parameter...

The problem of selecting pair-potentials of finite range for Gibbs random fields is considered as an important step in modelling multi-textured images. In a decision theoretic set-up, the Bayesian procedure is approximated by using Laplace's method for asymptotic expansion of integrals. Certain frequentist properties of the selection procedure are investigated. In particular, its consistency is...

Estimation of the parameters of Markov random field models for spatial and temporal data arises in many applications. There are computational and statistical challenges in developing efficient estimators because of the complexity of the joint distribution of the spatio-temporal models, especially when they involve hidden states that also need to be estimated from the observations. We develop co...