Spatiotemporal reconstructions of global CO 2 ‐fluxes using Gaussian Markov random fields
Sign Up to like & getrecommendations! Published in 2019 at "Environmetrics"
DOI: 10.1002/env.2610
Abstract: Atmospheric inverse modeling is a method for reconstructing historical fluxes of green‐house gas between land and atmosphere, using observed atmospheric concentrations and an atmospheric tracer transport model. The small number of observed atmospheric concentrations in… read more here.
Keywords: fluxes using; random fields; markov random; using gaussian ... See more keywords
An information-theoretic wavelet-based texture descriptor using Gaussian Markov random field models
Sign Up to like & getrecommendations! Published in 2019 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-019-07916-3
Abstract: Texture characterization and identification is a key issue for a variety of computer vision and image processing applications. Current techniques developed for dealing with the purpose thereof still present performance issues when applied in the… read more here.
Keywords: information; markov random; random field; texture descriptor ... See more keywords
Cauchy Markov random field priors for Bayesian inversion
Sign Up to like & getrecommendations! Published in 2022 at "Statistics and Computing"
DOI: 10.1007/s11222-022-10089-z
Abstract: The use of Cauchy Markov random field priors in statistical inverse problems can potentially lead to posterior distributions which are non-Gaussian, high-dimensional, multimodal and heavy-tailed. In order to use such priors successfully, sophisticated optimization and… read more here.
Keywords: random field; field priors; markov random; cauchy ... See more keywords
An Explicit Solution for Image Restoration using Markov Random Fields
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Signal Processing Systems"
DOI: 10.1007/s11265-019-01470-9
Abstract: Image restoration and denoising is an essential preprocessing step for almost every subsequent task in computer vision. Markov Random Fields offer a well-founded, sophisticated approach for this purpose, but unfortunately the associated computation procedures are… read more here.
Keywords: random fields; image restoration; markov random;
Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Automation and Computing"
DOI: 10.1007/s11633-017-1109-4
Abstract: This paper models the complex simultaneous localization and mapping (SLAM) problem through a very flexible Markov random field and then solves it by using the iterated conditional modes algorithm. Markovian models allow to incorporate: any… read more here.
Keywords: methodology; markov random; conditional modes; iterated conditional ... See more keywords
Spatially explicit modeling of community occupancy using Markov Random Field models with imperfect observation: Mesocarnivores in Apostle Islands National Lakeshore
Sign Up to like & getrecommendations! Published in 2021 at "Ecological Modelling"
DOI: 10.1016/j.ecolmodel.2021.109712
Abstract: Abstract How species organize spatially is one of ecology’s most motivating questions. Multiple theories have been advanced and various models developed to account for the environment, interactions among species, and spatial drivers. However, relative importance… read more here.
Keywords: national lakeshore; spatially explicit; markov random; community occupancy ... See more keywords
A Markov random field integrating spectral dissimilarity and class co-occurrence dependency for remote sensing image classification optimization
Sign Up to like & getrecommendations! Published in 2017 at "Isprs Journal of Photogrammetry and Remote Sensing"
DOI: 10.1016/j.isprsjprs.2017.03.020
Abstract: Abstract This paper develops a novel Markov Random Field (MRF) model for edge-preserving spatial regularization of classification maps. MRF methods based on the uniform smoothness lead to oversmoothed solutions. In contrast, MRF methods which take… read more here.
Keywords: classification; markov random; random field; remote sensing ... See more keywords
Integration of 3-dimensional discrete wavelet transform and Markov random field for hyperspectral image classification
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2016.11.034
Abstract: Hyperspectral image (HSI) classification is one of the fundamental tasks in HSI analysis. Recently, many approaches have been extensively studied to improve the classification performance, among which integrating the spatial information underlying HSIs is a… read more here.
Keywords: classification; markov random; dimensional discrete; wavelet transform ... See more keywords
A three-class change detection methodology for SAR-data based on hypothesis testing and Markov Random field modelling
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2017.1384590
Abstract: ABSTRACT This study presents a new automatic change detection process chain based on bi-temporal co-registered and calibrated Sentinel-1 level-1 Interferometric Wide Ground Range Detected C-band synthetic aperture radar intensity imagery. The whole processor contains three… read more here.
Keywords: methodology; detection; markov random; random field ... See more keywords
Segmentation for remote-sensing imagery using the object-based Gaussian-Markov random field model with region coefficients
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2018.1563841
Abstract: ABSTRACT The Markov random field (MRF) model is a widely used method for remote-sensing image segmentation, especially the object-based MRF (OMRF) method has attracted great attention in recent years. However, the OMRF method usually fails… read more here.
Keywords: markov random; object based; segmentation; remote sensing ... See more keywords
Exact enumeration approach to first-passage time distribution of non-Markov random walks.
Sign Up to like & getrecommendations! Published in 2019 at "Physical review. E"
DOI: 10.1103/physreve.99.062101
Abstract: We propose an analytical approach to study non-Markov random walks by employing an exact enumeration method. Using the method, we derive an exact expansion for the first-passage time (FPT) distribution of any continuous differentiable non-Markov… read more here.
Keywords: exact enumeration; random walks; first passage; non markov ... See more keywords