Foregrounds extracted from the background, which are intended to be used as photorealistic avatars for simulators in a variety of virtual worlds, should satisfy the following four requirements: 1) real-time implementation, 2) memory minimization, 3) reduced noise, and 4) clean boundaries. Accordingly, the present paper proposes a codebook-based Markov Random Field (MRF) model for background sub...

Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to incorporating only local interactions and cannot model global properties such as connectedness, which is a potentially useful high-level prior for object segmentation. In this work, we overcome this limitation by deri...

Magnetic resonance fingerprinting (MRF) is a novel technique that allows for the fast and simultaneous quantification of multiple tissue properties, progressing from qualitative images, such as T1or T2-weighted images commonly used in clinical routines, to quantitative parametric maps. MRF consists of two main elements: accelerated pseudorandom acquisitions that create unique signal evolutions ...

In this paper, we present a new image segmentation method based on energy minimization for iteratively evolving an implicit active contour. Methods for active contour evolution is important in many applications ranging from video post-processing to medical imaging, where a single object must be chosen from a multi-object collection containing objects sharing similar characteristics. Level set m...

The objective was to study the effect of maternal supplementation with a yeast cell wall-based product containing a mannan-rich fraction (MRF) during gestation and lactation on piglet intestinal gene expression. First parity sows were fed experimental gestation and lactation diets with or without MRF (900 mg/kg). After farrowing, piglets were fostered within treatment, as necessary. Sow and lit...

In interactive segmentation, the most common way to model object appearance is by GMM or histogram, while MRFs are used to encourage spatial coherence among the object labels. This makes the strong assumption that pixels within each object are i.i.d. when in fact most objects have multiple distinct appearances and exhibit strong spatial correlation among their pixels. At the very least, this ca...

Separation of sources from one-dimensional mixture signals such as speech has been largely explored. However, two-dimensional sources (images) separation problem has only been examined to a limited extent. The reason is that ICA is a very general-purpose statistical technique, and it does not take the spatial information into account while separating mixture images. In this paper, we introduce ...

We describe a learning procedure for a generative model that contains a hidden Markov Random Field (MRF) which has directed connections to the observable variables. The learning procedure uses a variational approximation for the posterior distribution over the hidden variables. Despite the intractable partition function of the MRF, the weights on the directed connections and the variational app...

This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation ...

A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is de-ned as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The latter relates to how data is observed and is problem domain dependent. The former depends on how var...