Search results for: Alan S. Willsky

Items from 1 to 20 out of 80 results

chapter

Multi-Sensor tracking of move-stop-move targets

Stefano P. Coraluppi, Craig A. Carthel, Alan S. Willsky

2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF) > 1 - 6

2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

This paper considers targets that exhibit move-stop-move motion patterns, for which we have kinematic-sensor detections. Our particular focus is how best to score the association of tracklets that result from a first stage of single-sensor kinematic tracking. The tracklets may originate from one or more sensors, and target-emission tracks may be available that typically exhibit low measurement rates...

chapter

Redundancy relations and robust failure detection

Edward Y. Chow, Xi-Cheng Lou, George C. Verghese, Alan S. Willsky

Lecture Notes in Control and Information Sciences > Detection of Abrupt Changes in Signals and Dynamical Systems > 275-293

All failure detection methods are based on the use of redundancy, that is on (possible dynamic) relations among the measured variables. Consequently the robustness of the failure detection process depends to a great degree on the reliability of the redundancy relations given the inevitable presence of model uncertainties. In this paper we address the problem of determining redundancy relations which...

chapter

A Unified Variational Approach to Denoising and Bias Correction in MR

Ayres Fan, William M. Wells, John W. Fisher, Müjdat Çetin, more

Lecture Notes in Computer Science > Information Processing in Medical Imaging > Posters I: Shape Modelling and Analysis > 148-159

We propose a novel bias correction method for magnetic resonance (MR) imaging that uses complementary body coil and surface coil images. The former are spatially homogeneous but have low signal intensity; the latter provide excellent signal response but have large bias fields. We present a variational framework where we optimize an energy functional to estimate the bias field and the underlying image...

chapter

Opportunities and challenges in signal processing and analysis

Alan S. Willsky

Lecture Notes in Computer Science > Future Tendencies in Computer Science, Control and Applied Mathematics > 331-338

This paper provides an overview of the author's perspective on the numerous challenges and opportunities in signal and image processing. In particular we focus for the most part on problems and critical issues for problems of statistical processing of spatially-distributed data. In addition to pointing to several applications areas of some importance we also describe a view of the technical challenges...

chapter

Hypothesis Testing over Factorizations for Data Association

Alexander T. Ihler, John W. Fisher, Alan S. Willsky

Lecture Notes in Computer Science > Information Processing in Sensor Networks > Information Processing in Sensor Networks > 239-253

The issue of data association arises frequently in sensor networks; whenever multiple sensors and sources are present, it may be necessary to determine which observations from different sensors correspond to the same target. In highly uncertain environments, one may need to determine this correspondence without the benefit of an a priori known joint signal/sensor model. This paper examines the data...

chapter

Incorporating Spatial Priors into an Information Theoretic Approach for fMRI Data Analysis

Junmo Kim, John W. Fisher, Andy Tsai, Cindy Wible, more

Lecture Notes in Computer Science > Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000 > Neuroimaging and Neurosurgery > 62-71

In previous work a novel information-theoretic approach was introduced for calculating the activation map for fMRI analysis [Tsai et al, 1999]. In that work the use of mutual information as a measure of activation resulted in a nonparametric calculation of the activation map. Nonparametric approaches are attractive as the implicit assumptions are milder than the strong assumptions of popular approaches...

chapter

Analysis of Functional MRI Data Using Mutual Information

Andy Tsai, John W. Fisher, Cindy Wible, William M. Wells, more

Lecture Notes in Computer Science > Medical Image Computing and Computer-Assisted Intervention – MICCAI’99 > Spatiotemporal and Diffusion Tensor Analysis > 473-480

A new information-theoretic approach is presented for analyzing fMRI data to calculate the brain activation map. The method is based on a formulation of the mutual information between two waveforms-the fMRI temporal response of a voxel and the experimental protocol timeline. Scores based on mutual information are generated for all voxels and then used to compute the activation map of an experiment...

chapter

Estimation-based approaches to rhythm analysis in electrocardiograms

Peter C. Doerschuk, Robert R. Tenney, Alan S. Willsky

Lecture Notes in Control and Information Sciences > Detection of Abrupt Changes in Signals and Dynamical Systems > 295-313

In this paper we provide an overview of research aimed at applying estimation methods to the problem of classifying rhythms and detecting arrhythmias in electrocardiograms. In particular we describe earlier work which produced a system which is quite successful in achieving its somewhat modest goals. We then describe our ongoing research which has as its goal a system capable of far finer interpretation...

chapter

Detection of abrupt changes in dynamic systems

Alan S. Willsky

Lecture Notes in Control and Information Sciences > Detection of Abrupt Changes in Signals and Dynamical Systems > 27-49

In this paper we present some of the basic ideas associated with the detection of abrupt changes in dynamic systems. Our presentation focuses on two classes of methods — multiple filter-based techniques and residual-based methods — and in far more detail on the multiple model and generalized likelihood ratio methods. Issues such as the effect of unknown onset time on algorithm complexity and structure...

chapter

Some structural properties of automata defined on groups

Roger W. Brockett, Alan S. Willsky

Lecture Notes in Computer Science > Category Theory Applied to Computation and Control > 112-118

chapter

Multiple time scale analysis of manufacturing systems

Adam Caromicoli, Alan S. Willsky, Stanley B. Gershwin

Lecture Notes in Control and Information Sciences > Analysis and Optimization of Systems > 677-688

In this paper we use results on the aggregation of singularly perturbed Markov chains to analyze manufacturing systems. The basis for this analysis is the presence in the system of events and processes that occur at markedly different rates — operations on machines, set-ups, failures, and repairs, etc. The result of the analysis is a set of models, each far simpler than the full model, describing...

chapter

MCMC Curve Sampling for Image Segmentation

Ayres C. Fan, John W. Fisher, William M. Wells, James J. Levitt, more

Lecture Notes in Computer Science > Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007 > General Medical Image Computing - III > 477-485

We present an algorithm to generate samples from probability distributions on the space of curves. We view a traditional curve evolution energy functional as a negative log probability distribution and sample from it using a Markov chain Monte Carlo (MCMC) algorithm. We define a proposal distribution by generating smooth perturbations to the normal of the curve and show how to compute the transition...

chapter

Scale space analysis by stabilized inverse diffusion equations

Ilya Pollak, Alan S. Willsky, Hamid Krim

Lecture Notes in Computer Science > Scale-Space Theory in Computer Vision > 200-211

We introduce a family of first-order multi-dimensional ordinary differential equations (ODEs) with discontinuous right-hand sides and demonstrate their applicability in image processing. An equation belonging to this family is an inverse diffusion everywhere except at local extrema, where some stabilization is introduced. For this reason, we call these equations “stabilized inverse diffusion equations”...

chapter

Aggregation and multi-level control in discrete event dynamic systems

Cüneyt M. Özveren, Alan S. Willsky

Lecture Notes in Control and Information Sciences > Analysis and Optimization of Systes > 947-956

In this paper we consider higher-level aggregate modelling and control of discrete-event dynamic systems (DEDS). The higher-level models considered correspond to associating specified sequences of events in the original system to single macroscopic events in the higher-level model. We also consider the problem of designing a compensator that can be used to restrict microscopic behavior so that the...

chapter

A Curve Evolution Approach to Medical Image Magnification via the Mumford-Shah Functional

Andy Tsai, Anthony Yezzi, Alan S. Willsky

Lecture Notes in Computer Science > Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000 > Segmentation > 246-255

In this paper, we introduce a curve evolution approach to image magnification based on a generalization of the Mumford-Shah functional. This work is a natural extension of the curve evolution implementation of the Mumford-Shah functional presented by the authors in previous work. In particular, by considering the image magnification problem as a structured case of the missing data problem, we generalize...

chapter

Coupled Multi-shape Model and Mutual Information for Medical Image Segmentation

A. Tsai, William M. Wells, Clare Tempany, E. Grimson, more

Lecture Notes in Computer Science > Information Processing in Medical Imaging > Segmentation > 185-197

This paper presents extensions which improve the performance of the shape-based deformable active contour model presented earlier in [9]. In contrast to that work, the segmentation framework that we present in this paper allows multiple shapes to be segmented simultaneously in a seamless fashion. To achieve this, multiple signed distance functions are employed as the implicit representations of the...

chapter

Nonparametric shape priors for active contour-based image segmentation

Junmo Kim, Mujdat Cetin, Alan S. Willsky

2005 13th European Signal Processing Conference > 1 - 4

2005 13th European Signal Processing Conference

When segmenting images of low quality or with missing data, statistical prior information about the shapes of the objects to be segmented can significantly aid the segmentation process. However, defining probability densities in the space of shapes is an open and challenging problem. In this paper, we propose a nonparametric shape prior model for image segmentation problems. In particular, given example...

article

Sampling From Gaussian Markov Random Fields Using Stationary and Non-Stationary Subgraph Perturbations

Ying Liu, Oliver Kosut, Alan S. Willsky

IEEE Transactions on Signal Processing > 2015 > 63 > 3 > 576 - 589

Gaussian Markov random fields (GMRFs) or Gaussian graphical models have been widely used in many applications. Efficiently drawing samples from GMRFs has been an important research problem. In this paper, we introduce the subgraph perturbation sampling algorithm, which makes use of any pre-existing tractable inference algorithm for a subgraph by perturbing this algorithm so as to yield asymptotically...

chapter

Semidefinite relaxations for optimization problems over rotation matrices

James Saunderson, Pablo A. Parrilo, Alan S. Willsky

53rd IEEE Conference on Decision and Control > 160 - 166

2014 IEEE 53rd Annual Conference on Decision and Control (CDC)

Optimization problems with variables constrained to be in SO(d)—orthogonal matrices with determinant one—arise in attitude estimation, molecular imaging, and computer vision applications, among others. Recently it has been shown that the convex hull of SO(d) can be described in terms of linear matrix inequalities. This allows us to devise new semidefinite programming-based reformulations and relaxations...

article

Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

Mujdat Cetin, Ivana Stojanovic, Ozben Onhon, Kush Varshney, more

IEEE Signal Processing Magazine > 2014 > 31 > 4 > 27 - 40

This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint...

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INFONA - science communication portal

Search results for: Alan S. Willsky

Multi-Sensor tracking of move-stop-move targets

Redundancy relations and robust failure detection

A Unified Variational Approach to Denoising and Bias Correction in MR

Opportunities and challenges in signal processing and analysis

Hypothesis Testing over Factorizations for Data Association

Incorporating Spatial Priors into an Information Theoretic Approach for fMRI Data Analysis

Analysis of Functional MRI Data Using Mutual Information

Estimation-based approaches to rhythm analysis in electrocardiograms

Detection of abrupt changes in dynamic systems

Some structural properties of automata defined on groups

Multiple time scale analysis of manufacturing systems

MCMC Curve Sampling for Image Segmentation

Scale space analysis by stabilized inverse diffusion equations

Aggregation and multi-level control in discrete event dynamic systems

A Curve Evolution Approach to Medical Image Magnification via the Mumford-Shah Functional

Coupled Multi-shape Model and Mutual Information for Medical Image Segmentation

Nonparametric shape priors for active contour-based image segmentation

Sampling From Gaussian Markov Random Fields Using Stationary and Non-Stationary Subgraph Perturbations

Semidefinite relaxations for optimization problems over rotation matrices

Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

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