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Title: Cover Estimation and Payload Location Using Markov Random Field.


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the SPIE Electronic Imaging held February 2-6, 2014 in San Francisco, CA.
Country of Publication:
United States

Citation Formats

Quach, Tu-Thach. Cover Estimation and Payload Location Using Markov Random Field.. United States: N. p., 2013. Web.
Quach, Tu-Thach. Cover Estimation and Payload Location Using Markov Random Field.. United States.
Quach, Tu-Thach. 2013. "Cover Estimation and Payload Location Using Markov Random Field.". United States. doi:.
title = {Cover Estimation and Payload Location Using Markov Random Field.},
author = {Quach, Tu-Thach},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2013,
month = 5

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  • This paper shows how to obtain unbiased samples from an unknown Markov chain by observing it for O(T{sub c}) steps, where T{sub c} is the cover time. This algorithm improves on several previous algorithms, and there is a matching lower bound. Using the techniques from the sampling algorithm, we also show how to sample random directed spanning trees from a weighted directed graph, with arcs directed to a root, and probability proportional to the product of the edge weights. This tree sampling algorithm runs within 18 cover times of the associated random walk, and is more generally applicable than themore » algorithm of Broder and Aldous.« less
  • The goal of next-generation seismic location is to ascertain a consistent set of event locations and travel-time corrections through simultaneous analysis of all relevant data. Towards that end, we are developing a new multiple-event location algorithm that utilizes the Markov-Chain Monte Carlo (MCMC) method for solving large, non-linear event inverse problems. Unlike most inverse methods, the MCMC approach produces a suite of solutions, each of which is consistent with seismic and other observations, as well as prior estimates of data and model uncertainties. In the MCMC multiple-event locator (MCMCloc), the model uncertainties consist of prior estimates on the accuracy ofmore » each input event location, travel-time prediction uncertainties, phase measurement uncertainties, and assessments of phase identification. The prior uncertainty estimates include correlations between travel-time predictions, correlations between measurement errors, and the probability of misidentifying one phase for another (or bogus picks). The implementation of prior constraints on location accuracy allows the direct utilization of ground-truth events in the location algorithm. This is a significant improvement over most other multiple-event locators (GMEL is an exception), for which location accuracy is achieved through post-processing comparisons with ground-truth information. Like the double-difference algorithm, the implementation of a correlation structure for travel-time predictions allows MCMCloc to operate over arbitrarily large geographic areas. MCMCloc can accommodate non-Gaussian and multi-modal pick distributions, which can enhance application to poorly recorded events. Further, MCMCloc allows for ambiguous determination of phase assignments, and the solution includes the probability that phases are properly assigned. The probabilities that phase assignments are correct are propagated to the estimates of all other model parameters. Posteriori estimates of event locations, path corrections, pick errors, and phase identifications are made through analysis of the posteriori suite of acceptable solutions. We test the MCMC locator on a regional data set of Nevada Test Site nuclear explosions. Event locations and origin times are known for these events, allowing us to tests the features of MCMCloc against a true ground truth (GT0) data set. Preliminary tests suggest that MCMCloc provides excellent relative locations (similar to other algorithms), and excellent absolute locations when constraints from one or more ground truth event are included. Tests also include realistic phase misidentification, where phase assignments are switched for phases that arrive within a few seconds of one another. We find that MCMCloc is a promising method for simultaneously locating large, geographically distributed data sets. Because we allow for input of prior knowledge on many aspects of the data set, MCMCloc is ideal for combining trusted and lesser-quality data.« less
  • In this paper a novel multiresolution wavelet analysis (MWA) and non-stationary Gaussian Markov random field (GMRF) technique is introduced for the identification of microcalcifications with high accuracy. The hierarchical multiresolution wavelet information in conjunction with the contextual information of the images extracted from GMRF provides a highly efficient technique for microcalcification detection. A Bayesian teaming paradigm realized via the expectation maximization (EM) algorithm was also introduced for edge detection or segmentation of larger lesions recorded on the mammograms. The effectiveness of the approach has been extensively tested with a number of mammographic images provided by a local hospital.
  • Cryo electron tomography (cryo-ET) is the primary method for obtaining 3D reconstructions of intact bacteria, viruses, and complex molecular machines ([7],[2]). It first flash freezes a specimen in a thin layer of ice, and then rotates the ice sheet in a transmission electron microscope (TEM) recording images of different projections through the sample. The resulting images are aligned and then back projected to form the desired 3-D model. The typical resolution of biological electron microscope is on the order of 1 nm per pixel which means that small imprecision in the microscope's stage or lenses can cause large alignment errors.more » To enable a high precision alignment, biologists add a small number of spherical gold beads to the sample before it is frozen. These beads generate high contrast dots in the image that can be tracked across projections. Each gold bead can be seen as a marker with a fixed location in 3D, which provides the reference points to bring all the images to a common frame as in the classical structure from motion problem. A high accuracy alignment is critical to obtain a high resolution tomogram (usually on the order of 5-15nm resolution). While some methods try to automate the task of tracking markers and aligning the images ([8],[4]), they require user intervention if the SNR of the image becomes too low. Unfortunately, cryogenic electron tomography (or cryo-ET) often has poor SNR, since the samples are relatively thick (for TEM) and the restricted electron dose usually results in projections with SNR under 0 dB. This paper shows that formulating this problem as a most-likely estimation task yields an approach that is able to automatically align with high precision cryo-ET datasets using inference in graphical models. This approach has been packaged into a publicly available software called RAPTOR-Robust Alignment and Projection estimation for Tomographic Reconstruction.« less
  • Advancements in noninvasive imaging methods such as X-ray computed tomography (CT) have led to a recent surge of applications in porous media research with objectives ranging from theoretical aspects of pore-scale fluid and interfacial dynamics to practical applications such as enhanced oil recovery and advanced contaminant remediation. While substantial efforts and resources have been devoted to advance CT technology, microscale analysis, and fluid dynamics simulations, the development of efficient and stable three-dimensional multiphase image segmentation methods applicable to large data sets is lacking. To eliminate the need for wet-dry or dual-energy scans, image alignment, and subtraction analysis, commonly applied inmore » X-ray micro-CT, a segmentation method based on a Bayesian Markov random field (MRF) framework amenable to true three-dimensional multiphase processing was developed and evaluated. Furthermore, several heuristic and deterministic combinatorial optimization schemes required to solve the labeling problem of the MRF image model were implemented and tested for computational efficiency and their impact on segmentation results. Test results for three grayscale data sets consisting of dry glass beads, partially saturated glass beads, and partially saturated crushed tuff obtained with synchrotron X-ray micro-CT demonstrate great potential of the MRF image model for three-dimensional multiphase segmentation. While our results are promising and the developed algorithm is stable and computationally more efficient than other commonly applied porous media segmentation models, further potential improvements exist for fully automated operation.« less