OBJECTIVE International guidelines propose prescribing sodium–glucose cotransporter 2 (SGLT2) inhibitors to patients with type diabetes (T2D) as secondary prevention in established atherosclerotic cardiovascular disease (ASCVD) or for primary of events high-risk multiple risk factors (MRF) ASCVD. The current analyses expand on the renal and metabolic effects SGLT2 MRF patients. RESEARCH DESIGN ...

This paper extends the MRF formulation approach developed in [7] and [6] for solving the shape from shading problem. Our method extends the Gibbs sampling approach to solve an MRF formulation which characterizes the Shape from Shading (SFS) problem under Lambertian reflectance conditions (the algorithm is extensible to other lighting models). Our method uses a simpler set of energy functions (o...

In this model we proposed on algorithm for object tracking using ‘graph –cut method’ and for labeling the pixels in graph-cut we use Markov random field (mrf) ,this theorem relies on Bayesian estimation associated with combinational optimization. The segmentation is obtained by classifying the pixels in to different pixel classes. These classes are represented by multivariate Gaussian distribut...

The original multiscale MRF texture model proposed by Paget (IEEE Transactions on Image Processing, 1998, page 925-931) can be used to synthesize a broad range of textures but is limited to taking a single input texture and outputting a homogeneous texture similar to the input. This is insufficient for textures that have combined visual characteristics from several different sources. To address...

This paper presents a novel image segmentation algorithm, which uses a biologically inspired paradigm known as swarm intelligence to segment images. A more efficient MRF based clustering algorithm that incorporated the Markov Random Field (MRF) theory and the Quantum Particle Swarm Optimization (QPSO) algorithm is proposed. the QPSO algorithm is ised to optimize the energy function which is a c...

We presented and evaluated a new Bayesian method for range image segmentation. The method proceeds in to stages. First, an initial segmentation was produced by a randomized region growing technique. The produced segmentation was considered as a degraded version of the ideal segmentation, which should be then refined. In the second stage, image pixels not labeled in the first stage were labeled ...

Herein, we propose a novel multi-layer Markov random field (MRF) image segmentation model which aims at combining color and texture features: Each feature is associated to a so called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation ...

A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural images and leads to excellent performance in the application of image denoising and inpainting. Moreover, the framework will be extended to image deblurring in our work. Instead of comm...

An improved MRF algorithm–hierarchical Gauss Markov Random Field model in the wavelet domain is presented for fabric image segmentation in this paper, which obtains the relation of inter-scale dependency from the feature field modeling and label field modeling. The Gauss-Markov random field modeling is usually adopted to feature field modeling. The label field modeling employs the inter-scale c...