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.

Parallel algorithms for phase unwrapping based on Markov random field models

Abstract: A general framework is presented for the design of parallel algorithms for two-dimensional, path-independent phase unwrapping of locally inconsistent, noisy principal-value phase fields that may contain regions of invalid information. This framework is based in Bayesian estimation theory with the use of Markov random field models to construct the prior distribution, so that the solution to the unwrapping problem is characterized as the minimizer of a piecewise-quadratic functional. This method allows one to design a variety of parallel algorithms with different computational properties, which simultaneously perform the desired path-independent unwrapping, interpolate over regions with invalid data, and reduce the noise. It is also shown how this approach may be extended to the case of discontinuous phase fields, incorporating information from fringe patterns of different frequencies.

Text/Non-text Ink Stroke Classification in Japanese Handwriting Based on Markov Random Fields

Abstract: In this paper, we present an approach for separating text and non-text ink strokes in online handwritten Japanese documents based on Markov random fields (MRFs), which effectively utilize the spatial relationship between strokes. Support vector machine (SVM) classifiers are trained for individual stroke and stroke pair classification, and on converting the SVM outputs to probabilities, the likelihood clique potentials of MRF are derived. In experiments on the TUAT Kon-date database, the proposed MRF approach yield superior performance compared to individual stroke classification and sequence classification based on hidden Markov models (HMMs).

Sign detection in natural images with conditional random fields

Abstract: Traditional generative Markov random fields for segmenting images model the image data and corresponding labels jointly, which requires extensive independence assumptions for tractability. We present the conditional random field for an application in sign detection, using typical scale and orientation selective texture filters and a nonlinear texture operator based on the grating cell. The resulting model captures dependencies between neighboring image region labels in a data-dependent way that escapes the difficult problem of modeling image formation, instead focusing effort and computation on the labeling task. We compare the results of training the model with pseudo-likelihood against an approximation of the full likelihood with the iterative tree reparameterization algorithm and demonstrate improvement over previous methods

Higher-order gradient descent by fusion-move graph cut

Abstract: Markov Random Field is now ubiquitous in many formulations of various vision problems. Recently, optimization of higher-order potentials became practical using higher-order graph cuts: the combination of i) the fusion move algorithm, ii) the reduction of higher-order binary energy minimization to first-order, and iii) the QPBO algorithm. In the fusion move, it is crucial for the success and efficiency of the optimization to provide proposals that fits the energies being optimized. For higher-order energies, it is even more so because they have richer class of null potentials. In this paper, we focus on the efficiency of the higher-order graph cuts and present a simple technique for generating proposal labelings that makes the algorithm much more efficient, which we empirically show using examples in stereo and image denoising.