Topic
Markov random field
About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.
Papers published on a yearly basis
Papers
More filters
••
01 Jan 1987TL;DR: Two models are given for the extraction of boundaries in digital images, one for discriminating textures and the other for discriminating objects, where a Markov random field is constructed as a prior distribution over intensities and labels.
Abstract: Two models are given for the extraction of boundaries in digital images, one for discriminating textures and the other for discriminating objects. In both cases a Markov random field is constructed as a prior distribution over intensities (observed) and labels (unobserved); the labels are either the texture types or boundary indicators. The posterior distribution, i.e., the conditional distribution over the labels given the intensities, is then analyzed by a Monte-Carlo algorithm called stochastic relaxation. The final labeling corresponds to a local maximum of the posterior likelihood.
35 citations
••
TL;DR: This paper model the interaction of illumination and geometry in the scene and associate it with image evidence for cast shadows using a higher order Markov Random Field (MRF) illumination model, and provides a two-stage minimization technique for the MRF energy of the model.
Abstract: The cast shadows in an image provide important information about illumination and geometry. In this paper, we utilize this information in a novel framework in order to jointly recover the illumination environment, a set of geometry parameters, and an estimate of the cast shadows in the scene given a single image and coarse initial 3D geometry. We model the interaction of illumination and geometry in the scene and associate it with image evidence for cast shadows using a higher order Markov Random Field (MRF) illumination model, while we also introduce a method to obtain approximate image evidence for cast shadows. Capturing the interaction between light sources and geometry in the proposed graphical model necessitates higher order cliques and continuous-valued variables, which make inference challenging. Taking advantage of domain knowledge, we provide a two-stage minimization technique for the MRF energy of our model. We evaluate our method in different datasets, both synthetic and real. Our model is robust to rough knowledge of geometry and inaccurate initial shadow estimates, allowing a generic coarse 3D model to represent a whole class of objects for the task of illumination estimation, or the estimation of geometry parameters to refine our initial knowledge of scene geometry, simultaneously with illumination estimation.
35 citations
••
TL;DR: This work proposes a regularized framework for the production of high-resolution (HR) DEM data with extended coverage by using a slope-based Markov random field regularization as the spatial regularization to deal with the registration error and the horizontal displacement among multi-scale measurements.
Abstract: The digital elevation model DEM is a significant digital representation of a terrain surface. Although a variety of DEM products are available, they often suffer from problems varying in spatial coverage, data resolution, and accuracy. However, the multi-source DEMs often contain supplementary information, which makes it possible to produce a higher-quality DEM through blending the multi-scale data. Inspired by super-resolution SR methods, we propose a regularized framework for the production of high-resolution HR DEM data with extended coverage. To deal with the registration error and the horizontal displacement among multi-scale measurements, robust data fidelity with weighted norm is employed to measure the conformance of the reconstructed HR data to the observed data. Furthermore, a slope-based Markov random field MRF regularization is used as the spatial regularization. The proposed method can simultaneously handle complex terrain features, noises, and data voids. Using the proposed method, we can reconstruct a seamless DEM data with the highest resolution among the input data, and an extensive spatial coverage. The experiments confirmed the effectiveness of the proposed method under different cases.
35 citations
••
TL;DR: This paper proposed a hierarchical approach, which combines a global contour-based line segment detection algorithm and an Markov random field model, to extract rectangular shape objects from real color images to show that this method is robust in locating multiple rectangular shape Objects simultaneously with respect to different size, orientation and color.
35 citations
••
TL;DR: Experimental results show that the heterogeneous clutter model introduced into the level set method for segmentation of high- resolution polarimetric synthetic aperture radar (PolSAR) images has a better capacity for characterizing high-resolution PolSAR data, especially for extremely heterogeneous regions.
Abstract: In this paper, a heterogeneous clutter model named ${\mathcal L}$ distribution is introduced into the level set method for segmentation of high-resolution polarimetric synthetic aperture radar (PolSAR) images. Level set methods are robust and effective techniques for segmentation. However, traditional level set methods for PolSAR data are based on the complex Wishart distribution, which is not an applicable model to high-resolution PolSAR images and heterogeneous regions such as forest and urban areas. The ${\mathcal L}$ distribution is proved to be a highly flexible model for multilook PolSAR data, which is based on the product model with a generalized-gamma-distributed texture component. The ${\mathcal L}$ -model-based level set segmentation method is assessed using C-band, X-band, and L-band PolSAR data acquired by RADARSAT-2, TerraSAR-X, and ESAR sensors, respectively. Experimental results show that the ${\mathcal L}$ -distribution model has a better capacity for characterizing high-resolution PolSAR data, especially for extremely heterogeneous regions. Compared with the Wishart-model-based and Kummer-U-model-based level set methods and Markov random field based methods, it is observed that the proposed level set algorithm can obtain more precise segmentation results.
35 citations