In this work, we propose a Markov random field-regulated Pitman–Yor process (MRF-PYP) prior for nonparametric clustering of data with spatial interdependencies. The MRF-PYP is constructed by imposing a Pitman–Yor process over the distribution of the latent variables that allocate data points to clusters (model states), the discount hyperparameter of which is regulated by an additionally postula...

-The most important task in digital image processing is image segmentation. This paper put forward an unique image segmentation algorithm that make use of a Markov Random Field (MRF) hybrid with biologically inspired technique Bacteria Foraging Optimization Algorithm (BFOA) for Brain Magnetic Resonance Images The proposed new algorithm works on the image pixel data and a region/neighborhood map...

The object of our study is the Bayesian approach in solving computer vision problems. We examine in particular: (i) applications of Markov random field (MRF) models to modeling spatial images; (ii) MRF based statistical methods for image restoration, segmentation, texture modeling and integration of different visual cues. Comments University of Pennsylvania Department of Computer and Informatio...

Markov random field (MRF) learning is intractable, and the approximation algorithms are computationally expensive. Since only a small subset of MRF is used frequently in computer vision, we characterize this subset with three concepts: (1) Lattice, (2) Homogeneity, and (3) Inertia; and design a non-markov high-bias low-variance model as an alternative to this subclass of MRF. Our goal is robust...

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...

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...

In the context of image segmentation, Markov random fields (MRF) are extensively used. However solution of MRF-based models is heavily dependent on how succesfully the MRF energy minimization is performed. In this light two methodologies, complementary to each other, are proposed for optimization of the special class of models comprising of a random field imposed on label priors. This class of ...

Magnetic resonance (MR) medical image segmentation plays an increasingly important role in computer-aided detection and diagnosis (CAD) of abnormalities. MRI segmentation manually is time consuming and consumes valuable human resources. Hence a great deal of efforts has been made to automate this process. Markov Random Field (MRF) has been one of the most active research areas of MRI brain segm...

Probabilistic-based methods have been used for designing noise tolerant circuits recently. In these methods, however, there is not any reliability mechanism that is essential for nanometer digital VLSI circuits. In this paper, we propose a novel method for designing reliable probabilistic-based logic gates. The advantage of the proposed method in comparison with previous probabilistic-based met...

-A new hybrid method is presented that combines the scale space filter (SSF) and Markov random field (MRF) for color image segmentation. The fundamental idea of the SSF is to use the convolution of Gaussian functions and image-histogram to generate a scale space image and then find the proper interval bounded by the local extrema of the derivatives. The Gaussian function is with zero mean and v...