Results 121 to 130 of about 47,039 (298)

Markov random field model selection [PDF]

Um campo aleatório de Markov é um grafo não-direcionado que expressa as dependências condicionais de um conjunto de variáveis aleatórias. Este trabalho visa realizar uma revisão do estado da arte de algoritmos de seleção de modelos para campos aleatórios
Carvalho, Rodrigo Ribeiro Santos de
core   +1 more source

The infinite hidden Markov random field model [PDF]

, 2010
Hidden Markov random field (HMRF) models are widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme is asked for.
Tsechpenakis, Gabriel, Chatzis, Sotirios P.   +1 more
core   +1 more source

Power spectral density and the brain

Canadian Journal of Statistics, EarlyView.
Abstract Time series from M/EEG (magneto/electroencephalography) and ECoG (electrocorticography) recordings are common sources of information about brain function. The power spectral density (PSD) preserves much of this information, up to second order. In the current decade, a burst of brain diagnostics using the slope of log(PSD) has appeared.
Priscilla E. Greenwood, Sveinung Erland, Lawrence M. Ward   +2 more
wiley   +1 more source

BIOMEDICAL IMAGE PROCESSING USING COMBINED MRF-CNN METHOD

Electrica, 2004
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.
Niyazi KILIC, Osman Nuri UCAN
doaj   +2 more sources

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes. [PDF]

Brief Bioinform, 2022
Yang Y, Shi X, Liu W, Zhou Q, Chan Lau M, Chun Tatt Lim J, Sun L, Ng CCY, Yeong J, Liu J.   +9 more
europepmc   +1 more source

Image de-noising using Markov Random Field in Wavelet Domain [PDF]

, 2014
Removing noise from original image is still a challenging problem for researchers. There have been several published algorithm and each approach has its assumptions, advantages and disadvantages.
Shweta Chaudhary, Prof A. L. Wanare
core  

The SIR Model in a Moving Population: Propagation of Infection and Herd Immunity

Communications on Pure and Applied Mathematics, EarlyView.
ABSTRACT In a collection of particles performing independent random walks on Zd$\mathbb {Z}^d$ we study the spread of an infection with SIR dynamics. Susceptible particles become infected when they meet an infected particle. Infected particles heal and are removed at rate ν$\nu$.
Duncan Dauvergne, Allan Sly
wiley   +1 more source

Integrating wavelet transformation with Markov random field analysis for the depth estimation of light‐field images

IET Computer Vision, 2017
This study addresses the problem of recovering the three‐dimensional depth data from the images taken by a light‐field camera. Unlike the conventional approach to extract the depth information from the spatial and the angular gradients in the epipolar ...
Wei‐Yu Lee, Chi‐Ying Li, Jia‐Yush Yen   +2 more
doaj   +1 more source

A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data. [PDF]

Brief Bioinform, 2022
Zhu B, Li H, Zhang L, Chandra SS, Zhao H.   +4 more
europepmc   +1 more source

Stochastic Gradient Descent in High Dimensions for Multi‐Spiked Tensor PCA

Communications on Pure and Applied Mathematics, EarlyView.
ABSTRACT We study the high‐dimensional dynamics of online stochastic gradient descent (SGD) for the multi‐spiked tensor model. This multi‐index model arises from the tensor principal component analysis (PCA) problem with multiple spikes, where the goal is to estimate the unknown signal vectors within the N$N$‐dimensional unit sphere through maximum ...
Gérard Ben Arous, Cédric Gerbelot, Vanessa Piccolo   +2 more
wiley   +1 more source