We propose a new segmentation or clustering model that combines Markov Random Field (MRF) and Normalized Cut (NC) objectives. Both NC and MRF models are widely used in machine learning and computer vision, but they were not combined before due to significant differences in the corresponding optimization, e.g. spectral relaxation and combinatorial max-flow techniques. On the one hand, we show th...

In this work we present Discriminative Random Fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the labels as well as the observed data. The discriminative random fields offer several advantages over the conventional Markov Random Field (MRF) framework. First, the DRFs allow to relax the strong assumption of condition...

The spatial distribution of gray level intensities in an image can be naturally modeled using Markov Random Field (MRF) models. We develop and investigate the performance of face detection algorithms derived from MRF considerations. For enhanced detection, the MRF models are defined for every permutation of site indices in the image. We find the optimal permutation that provides maximum discrim...

S.Z. Li, Y.H. Huang, J.S. Fu School of Electrical and Electronic Engineering, Nanyang Technological University Nanyang Avenue, Singapore 2263 ABSTRACT A general de nition of convex potential functions is given for discontinuity-preserving MRF restoration models. This gives a class of Bayesian MRF models which satisfy several desirable analytical and computational properties for regularization o...

Current routine MRI examinations rely on the acquisition of qualitative images that have a contrast "weighted" for a mixture of (magnetic) tissue properties. Recently, a novel approach was introduced, namely MR Fingerprinting (MRF) with a completely different approach to data acquisition, post-processing and visualization. Instead of using a repeated, serial acquisition of data for the characte...

OBJECTIVE Previous studies have demonstrated that immune complexes (ICs) may be involved in the pathogenesis of rheumatoid arthritis (RA). However, autoantigens contained in rheumatoid ICs remain to be elucidated. In the present study, we investigated whether the peptides captured by C1q and monoclonal rheumatoid factor (mRF), presumably associated with ICs, were citrullinated in synovial fluid...

Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel ...

A supervised classification method based on AdaBoost posterior probability and Markov Random Fields (MRF) model with Linear Targets Prior (LTP) is proposed in this paper. Firstly in contrast with most existing regions (superpixels) based models, this approach captures contiguous image regions called superpixels from ratio response maps of original images. Secondly, Adaboost classifier is employ...

In this paper, we analyze Markov Random Field (MRF) as a spatial regularizer in fMRI detection. The low signal-to-noise ratio (SNR) in fMRI images presents a serious challenge for detection algorithms, making regularization necessary to achieve good detection accuracy. Gaussian smoothing, traditionally employed to boost SNR, often produces over-smoothed activation maps. Recently, the use of MRF...

For many years, vehicle tracking in traffic images has suffered from the problems of occlusions and sudden variations in illumination. In order to resolve these occlusion problems, we have been proposing the Spatio-Temporal Markov Random Field model(S-T MRF) for segmentation of spatio-temporal images. This S-T MRF optimizes the segmentation boundaries of occluded vehicles and their motion vecto...