Results 41 to 50 of about 256,437 (324)
Bayesian mapping of brain regions using compound Markov random field priors [PDF]
, 2003 Human brain mapping, i.e. the detection of functional regions and their connections, has experienced enormous progress through the use of functional magnetic resonance imaging (fMRI).
Fahrmeir, Ludwig +2 more
core +2 more sources
Convolutional Neural Networks Grid Search Optimizer Based Brain Tumor Detection
International Transactions on Electrical Engineering and Computer Science, 2023 The brain tissues segmented by MRI and CT provide a more accurate viewpoint on diagnosing various brain illnesses. Many different segmentation approaches may be used to brain MRI images.
Pushpak Kurella
doaj
IEEE Access, 2022 A learning-based image relighting framework is proposed for automatically changing the lighting conditions of facial images from one lighting source to another. Given only a 2D unseen facial testing image, the framework automatically infers the highlight
Ching-Ting Tu +2 more
doaj +1 more source
Deep Markov Random Field for Image Modeling
, 2016 Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to ...
A Dempster +20 more
core +1 more source
Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models [PDF]
, 2013 This report considers the problem of computing the Cramer-Rao bound for the parameters of a Markov random field. Computation of the exact bound is not feasible for most fields of interest because their likelihoods are intractable and have intractable ...
Batatia, Hadj +3 more
core +7 more sources
CompositionalityMarkov random fields are known to be fully characterized by properties of their information diagrams, or I-diagrams. In particular, for Markov random fields, regions in the I-diagram corresponding to disconnected vertex sets in the graph vanish. Recently, I-diagrams have been generalized to F-diagrams, for a larger class of functions F satisfying the ...
Leon Lang +3 more
openaire +2 more sources
Improved adaptive Markov random field based super-resolution mapping for mangrove tree identification [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 Traditionally, forest tree crowns are extracted using airborne or spaceborne hyper-/multi-spectral remotely sensed images or pansharpened images. However, these medium/low spatial resolution images suffer from the mixed pixel problem, and the cost to ...
H. Aghighi +3 more
doaj +1 more source
Joint modeling of ChIP-seq data via a Markov random field model [PDF]
, 2013 Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments ...
Dhavala +9 more
core +2 more sources
Advanced Optical Materials, EarlyView.A dual‐domain PSLC architecture enables direct comparison of alignment‐dependent laser damage within a single device. Crack‐like and seal‐like morphologies emerge under different damage conditions, and their evolution is interpreted through quantitative image analysis and heat‐driven simulations.
Dengcheng Chen +4 more
wiley +1 more source
Hyperspectral image classification with deep 3D capsule network and Markov random field
IET Image Processing, 2022 To address the existing problems of capsule networks in deep feature extraction and spatial‐spectral feature fusion of hyperspectral images, this paper proposes a hyperspectral image classification method that combines a deep residual 3D capsule network ...
Xiong Tan +4 more
doaj +1 more source