In this paper, to improve image performance of biomedical data, Markov Random Field (MRF) and Cellular Neural Network (CNN) structures are combined and a new approach, Markov Random Field-Cellular Neural Networks (MRF-CNN) is introduced. MRF-CNN structure can be applied to biomedical data for various image processing problems such as noise filtering, edge detecting, blank filing etc., with nois...

The medullary reticular formation (MRF) of the neonatal mouse is organized so that the medial and lateral MRF activate hindlimb and trunk motoneurons (MNs) with differential predominance. The goal of the present study was to investigate whether this activation is polysynaptic and mediated by commissural interneurons with descending axons (dCINs) in the lumbar spinal cord. To this end, we tested...

MRF model is recognized as one of efficient tools for image classification. However, traditional MRF model prove to be limited for high resolution image classification. This paper presents a joint pixel and region based multi-scale MRF model for high resolution image classification. Based on initial image segmentation, the region shape information is integrated into MRF model to consider the pi...

In prior studies that used transneuronal transport of isogenic recombinants of pseudorabies virus, we established that medial medullary reticular formation (MRF) neurons sent collateralized projections to both diaphragm and abdominal muscle motoneurons. Furthermore, inactivation of MRF neurons in cats and ferrets increased the excitability of diaphragm and abdominal motoneurons, suggesting that...

This paper presents a Markov random eld (MRF) model for object recognition in high level vision. The labeling state of a scene in terms of a model object is considered as an MRF or couples MRFs. Within the Bayesian framework, the optimal solution is deened as the maximum a posteriori (MAP) estimate of the MRF. The posterior distribution is derived based on sound mathematical principles from the...

Markov random "eld (MRF) modeling is a popular pattern analysis method and MRF parameter estimation plays an important role in MRF modeling. In this paper, a method based on Markov Chain Monte Carlo (MCMC) is proposed to estimate MRF parameters. Pseudo-likelihood is used to represent likelihood function and it gives a good estimation result. ( 2000 Pattern Recognition Society. Published by Else...

Markov Random Field (MRF) models with potentials learned from the data have recently received attention for learning the low-level structure of natural images. A MRF provides a principled model for whole images, unlike ICA, which can in practice be estimated for small patches only. However, learning the filters in an MRF paradigm has been problematic in the past since it required computationall...