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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
11 Aug 1995
TL;DR: It is proposed to classify hierarchical MRF-based approaches as explicit and implicit methods, with appropriate subclasses, and several specific examples of each class of approach are described.
Abstract: The need for hierarchical statistical tools for modeling and processing image data, as well as the success of Markov random fields (MRFs) in image processing, have recently given rise to a significant research activity on hierarchical MRFs and their application to image analysis problems. Important contributions, relying on different models and optimization procedures, have thus been recorded in the literature. This paper presents a synthetic overview of available models and algorithms, as well as an attempt to clarify the vocabulary in this field. We propose to classify hierarchical MRF-based approaches as explicit and implicit methods, with appropriate subclasses. Each of these major classes is defined in the paper, and several specific examples of each class of approach are described.

68 citations

Proceedings ArticleDOI
03 Sep 2000
TL;DR: The dedicated algorithm, defined as spatio-temporal Markov random field model to traffic images at an intersection, was able to track vehicles at the intersection robustly against occlusions and segment and track such occluded vehicles at a high success rate.
Abstract: It is very important to achieve reliable vehicle tracking in ITS application such as accident detection. The most difficult problem associated with vehicle tracking is the occlusion effect among vehicles. In order to resolve this problem, we applied the dedicated algorithm which we defined as spatio-temporal Markov random field model to traffic images at an intersection. The spatio-temporal MRF considers texture correlations between consecutive images as well as the correlation among neighbors within a image. As a result, we were able to track vehicles at the intersection robustly against occlusions. Vehicles appear in various kinds of shapes and they move in random manners at the intersection. Although occlusions occur in such complicated manners, the algorithm given was able to segment and track such occluded vehicles at a high success rate of 93-96%. The algorithm requires only gray scale images and does not assume any physical models of vehicles.

68 citations

Proceedings ArticleDOI
20 Jun 2011
TL;DR: The results show that the learned NLR-MRF model significantly outperforms the traditional MRF models and produces state-of-the-art results.
Abstract: In this paper, we design a novel MRF framework which is called Non-Local Range Markov Random Field (NLR-MRF). The local spatial range of clique in traditional MRF is extended to the non-local range which is defined over the local patch and also its similar patches in a non-local window. Then the traditional local spatial filter is extended to the non-local range filter that convolves an image over the non-local ranges of pixels. In this framework, we propose a gradient-based discriminative learning method to learn the potential functions and non-local range filter bank. As the gradients of loss function with respect to model parameters are explicitly computed, efficient gradient-based optimization methods are utilized to train the proposed model. We implement this framework for image denoising and in-painting, the results show that the learned NLR-MRF model significantly outperforms the traditional MRF models and produces state-of-the-art results.

68 citations

Journal ArticleDOI
TL;DR: This paper proposes to characterize image regions locally by defining local region descriptors (LRDs), essentially feature statistics from pixels located within windows centered on the evolving contour, and they may reduce the overlap between distributions.
Abstract: Edge-based and region-based active contours are frequently used in image segmentation. While edges characterize small neighborhoods of pixels, region descriptors characterize entire image regions that may have overlapping probability densities. In this paper, we propose to characterize image regions locally by defining local region descriptors (LRDs). These are essentially feature statistics from pixels located within windows centered on the evolving contour, and they may reduce the overlap between distributions. LRDs are used to define general-form energies based on level sets. In general, a particular energy is associated with an active contour by means of the logarithm of the probability density of features conditioned on the region. In order to reduce the number of local minima of such energies, we introduce two novel functions for constructing the energy functional which are both based on the assumption that local densities are approximately Gaussian. The first uses a similarity measure between features of pixels that involves confidence intervals. The second employs a local Markov Random Field (MRF) model. By minimizing the associated energies, we obtain active contours that can segment objects that have largely overlapping global probability densities. Our experiments show that the proposed method can accurately segment natural large images in very short time when using a fast level-set implementation.

68 citations

20 Nov 1995
TL;DR: This thesis presents an integrated approach in modeling, extracting, detecting and classifying deformable contours directly from noisy images, using minimax principle to derive a regularization criterion whereby the values can be automatically and implicitly determined along the contour.
Abstract: This thesis presents an integrated approach in modeling, extracting, detecting and classifying deformable contours directly from noisy images. We begin by conducting a case study on regularization, formulation and initialization of the active contour models (snakes). Using minimax principle, we derive a regularization criterion whereby the values can be automatically and implicitly determined along the contour. Furthermore, we formulate a set of energy functionals which yield snakes that contain Hough transform as a special case. Subsequently, we consider the problem of modeling and extracting arbitrary deformable contours from noisy images. We combine a stable, invariant and unique contour model with Markov random field to yield prior distribution that exerts influence over an arbitrary global model while allowing for deformation. Under the Bayesian framework, contour extraction turns into posterior estimation, which is in turn equivalent to energy minimization in a generalized active contour model. Finally, we integrate these lower level visual tasks with pattern recognition processes of detection and classification. Based on the Nearman-Pearson lemma, we derive the optimal detection and classification tests. As the summation is peaked in most practical applications, only small regions need to be considered in marginalizing the distribution. The validity of our formulation have been confirmed by extensive and rigorous experimentations.

68 citations


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