Gaussian Markov random fields (GMRFs) are specified conditionally by its precision matrix meaning that its inverse, the covariance matrix, is not explicitly known. Computing the often dense covariance matrix directly using matrix inversion is often unfeasible due to time and memory requirement. In this note, we discuss a simple and fast algorithm to compute the marginal variances for a GMRF. We...

In this paper, we present an algorithm to identify types of places and objects from 2D and 3D laser range data obtained in indoor environments. Our approach is a combination of a collective classification method based on associative Markov networks together with an instance-based feature extraction using nearest neighbor. Additionally, we show how to select the best features needed to represent...

We derive an approximate maximum a posteriori (MAP) method for detecting NDE defect signals using hidden Markov random fields (HMRFs). In the proposed HMRF framework, a set of spatially distributed NDE measurements is assumed to form a noisy realization of an underlying random field that has a simple structure with Markovian dependence. Here, the random field describes the defect signals to be ...

We propose a method for the simultaneous construction of multiple image segmentations by combining a recently proposed “convolution of mixtures of Gaussians” model with a multi-layer hidden Markov random field structure. The resulting method constructs for a single image several, alternative segmentations that capture different structural elements of the image. We also apply the method to colle...

We propose a simple estimator based on composite likelihoods for parameter learning in random field models. The estimator can be applied to all discrete graphical models such as Markov random fields and conditional random fields, including ones with higher-order energies. It is computationally efficient because it requires only inference over treestructured subgraphs of the original graph, and ...

We propose a new method for detecting objects such as bags carried by pedestrians depicted in short video sequences. In common with earlier work [1,2] on the same problem, the method starts by averaging aligned foreground regions of a walking pedestrian to produce a representation of motion and shape (known as a temporal template) that has some immunity to noise in foreground segmentations and ...

Although much effort has been put into speech understanding systems there still exists a rather wide gap between acoustic recognition and linguistic interpretation. We propose a formalism for an extremely close interaction of acoustic recognition and higher level analysis. Instead of a strict horizontal interface at the level of hypothesized word sequences or lattices, a vertical interface to t...

Uterus, bladder and rectum are the maximally exposed organs during cervical cancer radiotherapy and are at high risk of radiation exposure. Estimation of dose accumulation in these organs across multiple fractions of external beam radiotherapy (EBRT) and brachytherapy (BT) is extremely challenging due to structural mis-correspondences and complex anatomical deformations between the EBRT and BT ...

The remarkable contribution by Weitz gives a general framework to establish the strong spatial mixing property of Gibbs measures. In light of Weitz’s work, we prove the strong spatial mixing for binary Markov random fields under the condition that the ‘external field’ is uniformly large or small by turning them into a corresponding Ising model. Our proof is done through a ‘path’ characterizatio...