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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: This work concludes that the texture feature based on Markov random field parameters combined with properly designed auxiliary features extracted from the texture context of the MCs can work outstandingly in the recognition of MCs in digital mammograms.

86 citations

Journal ArticleDOI
TL;DR: A supervised texture segmentation scheme is proposed in this article that results in an optimal segmentation of the textured image including images from remote sensing.
Abstract: A supervised texture segmentation scheme is proposed in this article. The texture features are extracted by filtering the given image using a filter bank consisting of a number of Gabor filters with different frequencies, resolutions, and orientations. The segmentation model consists of feature formation, partition, and competition processes. In the feature formation process, the texture features from the Gabor filter bank are modeled as a Gaussian distribution. The image partition is represented as a noncausal Markov random field (MRF) by means of the partition process. The competition process constrains the overall system to have a single label for each pixel. Using these three random processes, the a posteriori probability of each pixel label is expressed as a Gibbs distribution. The corresponding Gibbs energy function is implemented as a set of constraints on each pixel by using a neural network model based on Hopfield network. A deterministic relaxation strategy is used to evolve the minimum energy state of the network, corresponding to a maximum a posteriori (MAP) probability. This results in an optimal segmentation of the textured image. The performance of the scheme is demonstrated on a variety of images including images from remote sensing.

86 citations

Journal ArticleDOI
TL;DR: By implicitly encoding the (local and global) shape information into an SA-tree, a variety of vision tasks, e.g., shape recognition, comparison, and retrieval, can be performed in a more robust and efficient way via various tree-based algorithms.
Abstract: Representing shapes in a compact and informative form is a significant problem for vision systems that must recognize or classify objects. We describe a compact representation model for two-dimensional (2D) shapes by investigating their self-similarities and constructing their shape axis trees (SA-trees). Our approach can be formulated as a variational one (or, equivalently, as MAP estimation of a Markov random field). We start with a 2D shape, its boundary contour, and two different parameterizations for the contour (one parameterization is oriented counterclockwise and the other clockwise). To measure its self-similarity, the two parameterizations are matched to derive the best set of one-to-one point-to-point correspondences along the contour. The cost functional used in the matching may vary and is determined by the adopted self-similarity criteria, e.g., cocircularity, distance variation, parallelism, and region homogeneity. The loci of middle points of the pairing contour points yield the shape axis and they can be grouped into a unique free tree structure, the SA-tree. By implicitly encoding the (local and global) shape information into an SA-tree, a variety of vision tasks, e.g., shape recognition, comparison, and retrieval, can be performed in a more robust and efficient way via various tree-based algorithms. A dynamic programming algorithm gives the optimal solution in O(N/sup 1/), where N is the size of the contour.

86 citations

Journal ArticleDOI
TL;DR: A novel segmentation algorithm based on a Markov random field model and an extensive data analysis for determining relevant features for the classification problem is given and the reachability of a good classification rate is evaluated using the Random Forest method.
Abstract: We present in this article a new method on unsupervised semantic parsing and structure recognition in peri-urban areas using satellite images. The automatic “building” and “road” detection is based on regions extracted by an unsupervised segmentation method. We propose a novel segmentation algorithm based on a Markov random field model and we give an extensive data analysis for determining relevant features for the classification problem. The novelty of the segmentation algorithm lies on the class-driven vector data quantization and clustering and the estimation of the likelihoods given the resulting clusters. We have evaluated the reachability of a good classification rate using the Random Forest method. We found that, with a limited number of features, among them some new defined in this article, we can obtain good classification performance. Our main contribution lies again on the data analysis and the estimation of likelihoods. Finally, we propose a new method for completing the road network exploiting its connectivity, and the local and global properties of the road network.

86 citations

Journal ArticleDOI
TL;DR: A class of such models (the double Markov random field) for images composed of several textures is described, which is considered to be the natural hierarchical model for such a task.
Abstract: Markov random fields are used extensively in model-based approaches to image segmentation and, under the Bayesian paradigm, are implemented through Markov chain Monte Carlo (MCMC) methods. We describe a class of such models (the double Markov random field) for images composed of several textures, which we consider to be the natural hierarchical model for such a task. We show how several of the Bayesian approaches in the literature can be viewed as modifications of this model, made in order to make MCMC implementation possible. From a simulation study, conclusions are made concerning the performance of these modified models.

86 citations


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