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Most imaging systems are adversely affected by the errors in the observation model. One significant example is encountered in synthetic aperture radar (SAR) imaging. Inexact measurement of the distance between the SAR sensing platform and the scene center or random delays on the transmitted signal result in model errors. These errors appear as phase errors in the SAR data and they cause defocusing...
One of the fundamental requirements of a traffic management system is the ability to determine when an incident has occurred so that proper responses can be initiated. Most of the existing automatic incident detection techniques suffer from many limitations including their inability to detect incidents under non dense traffic conditions and generation of many false positive alarms. In this paper,...
We consider a decentralized estimation network subject to communication constraints such that nearby platforms can communicate with each other through low capacity links rendering an undirected graph. After transmitting symbols based on its measurement, each node outputs an estimate for the random variable it is associated with as a function of both the measurement and incoming messages from neighbors...
We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem,...
We consider the problem of decentralized estimation of a random-field under communication constraints in a Bayesian setting. The underlying system is composed of sensor nodes which collect measurements due to random variables they are associated with and which can communicate through finite-rate channels in accordance with a directed acyclic topology. After receiving the incoming messages if any,...
One of the fundamental problems in Synthetic Aperture Radar (SAR) imaging is phase errors. Phase errors occur when the time required for the transmitted signal from SAR to the target and back cannot be obtained properly either because the distance between the SAR platform and the target cannot be measured exactly or in the case of random delays in the signal due to propagation in atmospheric turbulence...
We propose a new approach for multi-sensor multi-target tracking by constructing statistical models on graphs with continuous-valued nodes for target states and discrete-valued nodes for data association hypotheses. These graphical representations lead to message-passing algorithms for the fusion of data across time, sensor, and target that are radically different than algorithms such as those found...
We propose a sparse signal representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature. On the other hand sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction...
This paper presents a new method for multiple structure segmentation, using a maximum a posteriori (MAP) estimation framework, based on prior shape densities involving nonparametric multivariate kernel density estimation of multiple shapes. Our method is motivated by the observation that neighboring or coupling structures in medical images generate configurations and co-dependencies which could potentially...
We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learning the LV boundary dynamics together with a particle-based inference algorithm on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the...
In this paper a new stereo-based 3D head tracking technique, based on scale-invariant feature transform (SIFT) features is proposed. A 3D head tracker is very important preprocessing for many vision applications. The proposed method is robust to out of plane rotations and translations and also invariant to sudden changes in time varying illumination. We present experiments to test the accuracy of...
The advance of computing technology has provided the means for building intelligent vehicle systems. Drowsy driver detection system is one of the potential applications of intelligent vehicle systems. Here we employ machine learning techniques to detect driver drowsiness. The system obtained 98% performance in predicting driver drowsiness. This is the highest prediction rate reported to date for detecting...
We consider the problem of hyper-parameter selection in advanced image reconstruction algorithms used in synthetic aperture radar (SAR) imaging. To deal with the parameter selection problem in these algorithms, we propose the use of unbiased predictive risk estimation and generalized cross-validation techniques. We demonstrate the effectiveness of the applied methods through experiments based on electromagnetically...
We consider the problem of localizing targets which act as acoustic sources over a region covered by a sensor network in which each node is equipped with an acoustic intensity sensor. The a posteriori distribution of each target location is constructed through a message passing algorithm on the factor graph representation of the joint posterior which is based on the loopy sum product algorithm. After...
This paper presents a new approach for segmentation of multiple brain structures. We introduce a new coupled shape prior for neighboring structures in magnetic resonance images (MRI) for multi object segmentation problem, where the information obtained from images can not provide enough contrast or exact boundary. In segmentation of low contrasted brain structures we take the advantage of using prior...
Sensor networks have provided a technology base for distributed target tracking applications among others. Conventional centralized approaches to the problem lack scalability in such a scenario where a large number of sensors provide measurements simultaneously under a possibly non-collaborating environment. Therefore research efforts have focused on scalable, robust, and distributed algorithms for...
The aging population in developed countries has shifted considerable research attention to diseases related to age. Because age is one of the highest risk factors for neurodegenerative diseases, the need for automated brain image analysis has significantly increased. Magnetic resonance imaging (MRI) is a commonly used modality to image brain. MRI provides high tissue contrast; hence, the existing...
We propose a shape and data driven texture segmentation method using local binary patterns (LBP) and active contours. In particular, we pass textured images through a new LBP-based filter, which produces non-textured images. In this "filtered" domain each textured region of the original image exhibits a characteristic intensity distribution. In this domain we pose the segmentation problem...
Vehicles instrumented with location tracking and wireless communication technologies (i.e., the so called probe vehicles) can serve as sensors for monitoring traffic conditions on transportation links. This paper is focused on estimating queue lengths in real-time at a signalized intersection approach based on the location and time data from probe vehicles that may constitute a given percentage of...
We consider the problem of wide-angle synthetic aperture radar (SAR) imaging from data with arbitrary frequency-band omissions. We propose an approach that involves composite image formation through combination of subaperture images, as well as point-enhanced, super-resolution image reconstruction. This framework provides a number of desirable features including preservation of anisotropic scatterers...
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