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.
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23 Jun 2008TL;DR: The algorithm is automatic, unsupervised, and efficient at producing smooth segmentation regions on many non-ideal iris images and a comparison of the estimated iris region parameters with the ground truth data is provided.
Abstract: A non-ideal iris segmentation approach using graph cuts is presented. Unlike many existing algorithms for iris localization which extensively utilize eye geometry, the proposed approach is predominantly based on image intensities. In a step-wise procedure, first eyelashes are segmented from the input images using image texture, then the iris is segmented using grayscale information, followed by a post-processing step that utilizes eye geometry to refine the results. A preprocessing step removes specular reflections in the iris, and image gradients in a pixel neighborhood are used to compute texture. The image is modeled as a Markov random field, and a graph cut based energy minimization algorithm [2] is used to separate textured and untextured regions for eyelash segmentation, as well as to segment the pupil, iris, and background using pixel intensity values. The algorithm is automatic, unsupervised, and efficient at producing smooth segmentation regions on many non-ideal iris images. A comparison of the estimated iris region parameters with the ground truth data is provided.
85 citations
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TL;DR: This work proposes a bottom-up approach to the problem of detecting general text in images, which reflects the characterness of an image region, and develops three novel cues that are tailored for character detection and a Bayesian method for their integration.
Abstract: Text in an image provides vital information for interpreting its contents, and text in a scene can aid a variety of tasks from navigation to obstacle avoidance and odometry. Despite its value, however, detecting general text in images remains a challenging research problem. Motivated by the need to consider the widely varying forms of natural text, we propose a bottom-up approach to the problem, which reflects the characterness of an image region. In this sense, our approach mirrors the move from saliency detection methods to measures of objectness. In order to measure the characterness, we develop three novel cues that are tailored for character detection and a Bayesian method for their integration. Because text is made up of sets of characters, we then design a Markov random field model so as to exploit the inherent dependencies between characters. We experimentally demonstrate the effectiveness of our characterness cues as well as the advantage of Bayesian multicue integration. The proposed text detector outperforms state-of-the-art methods on a few benchmark scene text detection data sets. We also show that our measurement of characterness is superior than state-of-the-art saliency detection models when applied to the same task.
85 citations
01 Oct 1987
TL;DR: A scheme to integrate intensity edges with stereo depth and motion field information and results from a Connection Machine algorithm are shown, showing the use of intensity edges to integrate other visual cues and to help discover discontinuities emerges as a general and powerful principle.
Abstract: Integration of several vision modules is likely to be one of the keys to the power and robustness of the human visual system. We suggest that integration is best performed at the location of discontinuities in early processes, such as discontinuities in image brightness, depth, motion, texture, and color. Coupled Markov Random Field models can be used to combine vision modalities with their discontinuities. We derive a scheme to integrate intensity edges with stereo depth and motion field information and show results from a Connection Machine algorithm on synthetic and natural images. The use of intensity edges to integrate other visual cues and to help discover discontinuities emerges as a general and powerful principle.
85 citations
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TL;DR: In this article, the authors address the problem of constructing and identifying a valid joint probability density function from a set of specified conditional densities, based on the development of relations between the joint and the conditional density using Markov random fields (MRFs).
84 citations
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TL;DR: In this article, a new low rank matrix factorization (LRMF) model was proposed by assuming noise as mixture of exponential power (MoEP) distributions and then proposed a penalized MoEP (PMoEP) model by combining the penalized likelihood method with MoEP distributions.
Abstract: Many computer vision problems can be posed as learning a low-dimensional subspace from high-dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problems using $L_{1}$ -norm and $L_{2}$ -norm losses, which mainly deal with the Laplace and Gaussian noises, respectively. To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as mixture of exponential power (MoEP) distributions and then proposes a penalized MoEP (PMoEP) model by combining the penalized likelihood method with MoEP distributions. Such setting facilitates the learned LRMF model capable of automatically fitting the real noise through MoEP distributions. Each component in this mixture distribution is adapted from a series of preliminary super- or sub-Gaussian candidates. Moreover, by facilitating the local continuity of noise components, we embed Markov random field into the PMoEP model and then propose the PMoEP-MRF model. A generalized expectation maximization (GEM) algorithm and a variational GEM algorithm are designed to infer all parameters involved in the proposed PMoEP and the PMoEP-MRF model, respectively. The superiority of our methods is demonstrated by extensive experiments on synthetic data, face modeling, hyperspectral image denoising, and background subtraction.
84 citations