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.

Toeplitz Inverse Covariance-based Clustering of Multivariate Time Series Data.

Abstract: Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (ie, walking, sitting, running) However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC) Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster Based on this graphical representation, TICC simultaneously segments and clusters the time series data We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios

Deep Residual Learning for Model-Based Iterative CT Reconstruction Using Plug-and-Play Framework

Abstract: Model-Based Iterative Reconstruction (MBIR) has shown promising results in clinical studies as they allow significant dose reduction during CT scans while maintaining the diagnostic image quality. MBIR improves the image quality over analytical reconstruction by modeling both the sensor (e.g., forward model) and the image being reconstructed (e.g., prior model). While the forward model is typically based on the physics of the sensor, accurate prior modeling remains a challenging problem. Markov Random Field (MRF) has been widely used as prior models in MBIR due to simple structure, but they cannot completely capture the subtle characteristics of complex images. To tackle this challenge, we generate a prior model by learning the desirable image property from a large dataset. Toward this, we use Plug-and-Play (PnP) framework which decouples the forward model and the prior model in the optimization procedure, replacing the prior model optimization by a image denoising operator. Then, we adopt the state-of-the-art deep residual learning for the image denoising operator which represents the prior model in MBIR. Experimental results on real CT scans demonstrate that our PnP MBIR with deep residual learning prior significantly reduces the noise and artifacts compared to analytical reconstruction and standard MBIR with MRF prior.

Motion estimation with detection of occlusion areas

Abstract: A method for estimation of motion vector fields that includes detection of motion discontinuities and occlusion areas is presented. A cost function is formulated that incorporates coupled Markov random field models for the motion vector field, the motion discontinuity field and the occlusion field. The cost function is minimized by a deterministic iterative process. Application of this method to an image sequence with occlusion is described. Results of motion-compensated temporal interpolation for 4:1 field subsampling are presented and compared to motion estimation without occlusion processing. >

Markov Random Field Based Text Identification from Annotated Machine Printed Documents

Abstract: In this paper, we describe an approach to segment handwritten text, machine printed text and noise from annotated machine printed documents. Three categories of word level features are extracted. We use a modified K-Means clustering algorithm for classification followed by a relabeling procedure using Markov Random Field(MRF) based on a concept of neighboring patches and Belief Propagation(BP)rules. Experimental results on an imbalanced data set show that our approach achieves an overall recall of 96.33% .

Real-Time Implementations of an MRF-based Motion Detection Algorithm

Abstract: The main concern in image processing is the computation cost. Markov random field (MRF)-based algorithms particularly require a significant amount of computation. The paper investigates three solutions to implement a simple, but robust, MRF-based motion detection algorithm in real time: SIMD machine, DSP-based image processing board, and analog resistive network. Details and performances of each implementation are given and a comparison between each realization is made. The underlying goal of this work is to study if real-time implementations of MRF-based algorithms are feasible or not. The answer is positive in the case of quite simple algorithms, but reserved with more complex ones.