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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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Proceedings ArticleDOI
01 Jul 2017
TL;DR: This work proposes a novel way of efficiently localizing a sports field from a single broadcast image of the game, and demonstrates the effectiveness of the method by applying it to soccer and hockey.
Abstract: In this work, we propose a novel way of efficiently localizing a sports field from a single broadcast image of the game. Related work in this area relies on manually annotating a few key frames and extending the localization to similar images, or installing fixed specialized cameras in the stadium from which the layout of the field can be obtained. In contrast, we formulate this problem as a branch and bound inference in a Markov random field where an energy function is defined in terms of semantic cues such as the field surface, lines and circles obtained from a deep semantic segmentation network. Moreover, our approach is fully automatic and depends only on a single image from the broadcast video of the game. We demonstrate the effectiveness of our method by applying it to soccer and hockey.

95 citations

Journal ArticleDOI
01 Aug 1992
TL;DR: An algorithm which circumvents this problem by updating connected groups of pixels formed in an intermediate segmentation step is proposed, which substantially increased the rate of convergence and the quality of the reconstruction.
Abstract: The authors present a method for nondifferentiable optimization in maximum a posteriori estimation of computed transmission tomograms. This problem arises in the application of a Markov random field image model with absolute value potential functions. Even though the required optimization is on a convex function, local optimization methods, which iteratively update pixel values, become trapped on the nondifferentiable edges of the function. An algorithm which circumvents this problem by updating connected groups of pixels formed in an intermediate segmentation step is proposed. Experimental results showed that this approach substantially increased the rate of convergence and the quality of the reconstruction. >

95 citations

Patent
23 Jan 2002
TL;DR: In this article, a new system and method for synthesizing textures from an input sample is presented, which uses a unique accelerated patch-based sampling system to synthesize high-quality textures in real-time using a small input texture sample.
Abstract: The present invention involves a new system and method for synthesizing textures from an input sample. A system and method according to the present invention uses a unique accelerated patch-based sampling system to synthesize high-quality textures in real-time using a small input texture sample. The patch-based sampling system of the present invention works well for a wide variety of textures ranging from regular to stochastic. Potential feature mismatches across patch boundaries are avoided by sampling patches according to a non-parametric estimation of the local conditional Markov Random Field (MRF) density function.

95 citations

Journal ArticleDOI
TL;DR: A Markov random field based multivariate segmentation algorithm called “multivariate iterative region growing using semantics” (MIRGS) is presented, which reduces the impact of intraclass variation and computational cost and improves segmentation effectiveness.
Abstract: Multivariate image segmentation is a challenging task, influenced by large intraclass variation that reduces class distinguishability as well as increased feature space sparseness and solution space complexity that impose computational cost and degrade algorithmic robustness. To deal with these problems, a Markov random field (MRF) based multivariate segmentation algorithm called “multivariate iterative region growing using semantics” (MIRGS) is presented. In MIRGS, the impact of intraclass variation and computational cost are reduced using the MRF spatial context model incorporated with adaptive edge penalty and applied to regions. Semantic region growing starting from watershed over-segmentation and performed alternatively with segmentation gradually reduces the solution space size, which improves segmentation effectiveness. As a multivariate iterative algorithm, MIRGS is highly sensitive to initial conditions. To suppress initialization sensitivity, it employs a region-level k -means (RKM) based initialization method, which consistently provides accurate initial conditions at low computational cost. Experiments show the superiority of RKM relative to two commonly used initialization methods. Segmentation tests on a variety of synthetic and natural multivariate images demonstrate that MIRGS consistently outperforms three other published algorithms.

94 citations

Journal ArticleDOI
TL;DR: The iterated conditional modes (ICM) framework for the optimization of the maximum a posteriori (MAP-MRF) criterion function is extended to include a nonlocal probability maximization step, which has the potential to preserve spatial details and to reduce speckle effects.
Abstract: In remote sensing change detection, Markov random field (MRF) has been used successfully to model the prior probability using class-labels dependencies MRF has played an important role in the detection of complex urban changes using optical images However, the preservation of details in urban change analysis turns out to be a highly complex task if multitemporal SAR images with their speckle are to be used Here, the ability of MRF to preserve geometric details and to combat speckle effect at the same time becomes questionable Blob-region phenomenon and fine structures removal are common consequences of the application of traditional MRF-based change detection algorithm To overcome these limitations, the iterated conditional modes (ICM) framework for the optimization of the maximum a posteriori (MAP-MRF) criterion function is extended to include a nonlocal probability maximization step This probability model, which characterizes the relationship between pixels’ class-labels in a nonlocal scale, has the potential to preserve spatial details and to reduce speckle effects Two multitemporal SAR datasets were used to assess the proposed algorithm Experimental results using three density functions [ie, the log normal (LN), generalized Gaussian (GG), and normal distributions (ND)] have demonstrated the efficiency of the proposed approach in terms of detail preservation and noise suppression Compared with the traditional MRF algorithm, the proposed approach proved to be less-sensitive to the value of the contextual parameter and the chosen density function The proposed approach has also shown less sensitivity to the quality of the initial change map when compared with the ICM algorithm

93 citations


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