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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
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Journal ArticleDOI
TL;DR: New methods for classifying key features (power lines, pylons, and buildings) comprising utility corridor scene using airborne LiDAR data and modelling power lines in 3D object space are introduced.
Abstract: This study aims to introduce new methods for classifying key features (power lines, pylons, and buildings) comprising utility corridor scene using airborne LiDAR data and modelling power lines in 3D object space. The proposed approach starts from PL scene segmentation using Markov Random Field (MRF), which emphasizes on the roles of spatial context of linear and planar features as in a graphical model. The MRF classifier identifies power line features from linear features extracted from given corridor scenes. The non-power line objects are then investigated in a planar space to sub-classify them into building and non-building class. Based on the classification results, precise localization of individual pylons is conducted through investigating a prior knowledge of contextual relations between power line and pylon. Once the pylon localization is accomplished, a power line span is identified, within which power lines are modelled with catenary curve models in 3D. Once a local catenary curve model is established, this initial model progressively extends to capture entire power line points by adopting model hypothesis and verification. The model parameters are adjusted using a stochastic non-linear square method for producing 3D power line models. An evaluation of the proposed approach is performed over an urban PL corridor area that includes a complex PL scene.

68 citations

Journal ArticleDOI
TL;DR: An original multiview stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives and is compared to state-of-the-art multIView stereo meshing algorithms.
Abstract: We present an original multiview stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives. The method provides a compact model while preserving details: Irregular elements such as statues and ornaments are described by meshes, whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones, and tori). We adopt a two-step strategy consisting first in segmenting the initial mesh-based surface using a multilabel Markov Random Field-based model and second in sampling primitive and mesh components simultaneously on the obtained partition by a Jump-Diffusion process. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e., geometry and shape layout). The segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation. Experimental results on complex urban structures and large scenes are presented and compared to state-of-the-art multiview stereo meshing algorithms.

67 citations

Book ChapterDOI
01 Jan 1997
TL;DR: In this paper, a statistical regularization approach based on multiscale Markov random field (MRF) models is proposed to detect regions whose apparent motion in the image is not conforming to the dominant motion of the background resulting from the camera movement.
Abstract: We present a statistical method to detect regions whose apparent motion in the image is not conforming to the dominant motion of the background resulting from the camera movement. Alternatively, the same scheme can be used to track a particular region of interest of the scene. The apparent motion induced by the camera motion is represented by a 2D parametric motion model, and compensated for using the values of the motion model parameters estimated by a multiresolution robust statistical technique. Then, regions whose motion cannot be described by this global model estimated over the entire image, are extracted. The detection of these non conforming regions is achieved through a statistical regularization approach based on multiscale Markov random field (MRF) models. We have paid a particular attention to the definition of the energy function involved and to the observations taken into account. To gain robustness, information is integrated over time. This method has been validated by experiments carried out on many real image sequences.

67 citations

Journal ArticleDOI
Jiawan Zhang1, Liang Li1, Yi Zhang1, Guoqiang Yang1, Xiaochun Cao1, Jizhou Sun1 
TL;DR: A new framework for video dehazing, the process of restoring the visibility of the videos taken under foggy scenes, which builds upon techniques in single image dehaze, optical flow estimation and Markov random field to improve the temporal and spatial coherence of the dehazed video.
Abstract: This paper describes a new framework for video dehazing, the process of restoring the visibility of the videos taken under foggy scenes. The framework builds upon techniques in single image dehazing, optical flow estimation and Markov random field. It aims at improving the temporal and spatial coherence of the dehazed video. In this framework, we first extract the transmission map frame-by-frame using guided filter, then estimate the forward and backward optical flow between two neighboring frames to find the matched pixels. The flow fields are used to help us building an MRF model on the transmission map to improve the spatial and temporal coherence of the transmission. The proposed algorithm is verified in both real and synthetic videos. The results demonstrate that our algorithm can preserve the spatial and temporal coherence well. With more coherent transmission map, we get better refocusing effect. We also apply our framework on improving the video coherence on the application of video denoising.

67 citations

Book ChapterDOI
01 Oct 2006
TL;DR: A new method of information integration in a graph based framework where tissue priors and local boundary information are integrated into the edge weight metrics in the graph and inhomogeneity correction is incorporated by adaptively adjusting the edge weights according to the intermediate inhomogeneous estimation.
Abstract: Brain MRI segmentation remains a challenging problem in spite of numerous existing techniques. To overcome the inherent difficulties associated with this segmentation problem, we present a new method of information integration in a graph based framework. In addition to image intensity, tissue priors and local boundary information are integrated into the edge weight metrics in the graph. Furthermore, inhomogeneity correction is incorporated by adaptively adjusting the edge weights according to the intermediate inhomogeneity estimation. In the validation experiments of simulated brain MRIs, the proposed method outperformed a segmentation method based on iterated conditional modes (ICM), which is a commonly used optimization method in medical image segmentation. In the experiments of real neonatal brain MRIs, the results of the proposed method have good overlap with the manual segmentations by human experts.

67 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
202330
2022128
202196
2020173
2019204