In this paper we propose a Markov Random Field based Automatic Registration method. This is an elastic registration method that uses the combination of saliency and gradient information. This Intensity-based registration of images is done by linear transformations, based on a discrete Markov random field (MRF) formulation. Here, the challenge arises from the fact that optimizing the energy asso...

For many years, object tracking in images has suffered from the problems of occlusions and illumination effects. In order to resolve occlusion problems, we have been proposing the Spatio-Temporal Markov Random Field model for segmentation of spatio-temporal images since 2000. This S-T MRF optimizes the segmentation boundaries of occluded objects and their motion vectors simultaneously, by refer...

In this paper, we propose a new formulation of the Differential Optical Flow Equation (DOFE) between two consecutive images considering spatial and temporal information from both. The displacement field is computed in a Markov Random Field (MRF) framework. The solution is done by minimization of the Gibbs energy using a Direct Descent Energy (DDE) algorithm. A hybrid multiresolution approach, c...

Noise in digital logic circuits does not reduce with the scaling down of CMOS devices. The conventional CMOS design does not provide noise immunity when the circuits are operated in the sub threshold region. In order to enhance the performance of the circuit and to handle the errors caused due to noise that are random and dynamic in nature, a cost effective probabilistic based noise tolerant ci...

This paper presents a two-pass algorithm for estimating motion vectors from image sequences. In the proposed algorithm, the motion estimation is formulated as a problem of obtaining the maximum a posteriori in the Markov random field (MAP-MRF). An optimization method based on the mean field theory (MFT) is opted to conduct the MAP search. The estimation of motion vectors is modeled by only two ...

OBJECTIVE Markov random field (MRF) models have been traditionally applied to the task of robust-to-noise image segmentation. Most approaches estimate MRF parameters on the whole image via a global expectation-maximization (EM) procedure. The resulting estimated parameters are likely to be uncharacteristic of local image features. Instead, we propose to distribute a set of local MRF models with...

We take beneet from a causal Markov model deened on a quadtree to derive a multiresolution EM algorithm for unsupervised image classiication. This algorithm is an eecient alternative to expensive or approximate EM algorithms associated with Markov Random Fields. We show on synthetic and real images that our algorithm also provides good or even better results than those obtained by spatial MRF m...

Bayesian network (BN) A directed graph whose nodes represent variables, and edges represent influences. Together with conditional probability distributions, a Bayesian network represents the joint probability distribution of its variables. Conditional probability distribution Assignment of probabilities to all instances of a set of variables when the value of one or more variables is known. Con...

There are many local and greedy algorithms for energy minimization over Markov Random Field (MRF) such as iterated condition mode (ICM) and various gradient descent methods. Local minima solutions can be obtained with simple implementations and usually require smaller computational time than global algorithms. Also, methods such as ICM can be readily implemented in a various difficult problems ...

We consider the question of computing Maximum A Posteriori (MAP) assignmentin an arbitrary pair-wise Markov Random Field (MRF). We present a randomizediterative algorithm based on simple local updates. The algorithm, starting with anarbitrary initial assignment, updates it in each iteration by first, picking a randomnode, then selecting an (appropriately chosen) random local nei...