27 results on '"Miller, Eric L."'
Search Results
2. Fast computation of the acoustic field for ultrasound elements
- Author
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Guven, H. Emre, Miller, Eric L., and Cleveland, Robin O.
- Subjects
Potential theory (Mathematics) -- Usage ,Transducers -- Design and construction ,Ultrasound imaging -- Analysis ,Business ,Electronics ,Electronics and electrical industries - Abstract
A fast method is described for computing the acoustic field of ultrasound transducers with application to rectangular elements that are cylindrically focused. The efficiency is gained by using a separable approximation to the Green's function and the tradeoff between accuracy and spatial sampling rate is analyzed for determining appropriate parameters for a specific transducer.
- Published
- 2009
3. Combined optical imaging and mammography of the healthy breast: optical contrast derived from breast structure and compression
- Author
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Fang, Qianqian, Carp, Stefan A., Selb, Juliette, Boverman, Greg, Zhang, Quan, Kopans, Daniel B., Moore, Richard H., Miller, Eric L., Brooks, Dana H., and Boas, David A.
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Diagnostic imaging -- Analysis ,Medical equipment -- Analysis ,Physiological apparatus -- Analysis ,Business ,Electronics ,Electronics and electrical industries ,Health care industry - Abstract
In this paper, we report new progress in developing the instrument and software platform of a combined X-ray mammography/diffuse optical breast imaging system. Particularly, we focus on system validation using a series of balloon phantom experiments and the optical image analysis of 49 healthy patients. Using the finite-element method for forward modeling and a regularized Gauss-Newton method for parameter reconstruction, we recovered the inclusions inside the phantom and the hemoglobin images of the human breasts. An enhanced coupling coefficient estimation scheme was also incorporated to improve the accuracy and robustness of the reconstructions. The recovered average total hemoglobin concentration (HbT) and oxygen saturation (S[O.sub.2]) from 68 breast measurements are 16.2 [micro]m and 71%, respectively, where the HbT presents a linear trend with breast density. The low HbT value compared to literature is likely due to the associated mammographic compression. From the spatially co-registered optical/X-ray images, we can identify the chest-wall muscle, fatty tissue, and fibroglandular regions with an average HbT of 20.1 [+ or -] 6.1 [micro]m for fibroglandular tissue, 15.4 [+ or -] 5.0 [micro]m for adipose, and 22.2 [+ or -] 7.3 [micro]m for muscle tissue. The differences between fibroglandular tissue and the corresponding adipose tissue are significant (p < 0.0001). At the same time, we recognize that the optical images are influenced, to a certain extent, by mammographicai compression. The optical images from a subset of patients show composite features from both tissue structure and pressure distribution. We present mechanical simulations which further confirm this hypothesis. Index Terms--Breast imaging, multimodality imaging, tomograhy.
- Published
- 2009
4. Estimation and statistical bounds for three-dimensional polar shapes in diffuse optical tomography
- Author
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Boverman, Gregory, Miller, Eric L., Brooks, Dana H., Isaacson, David, Fang, Qianqian, and Boas, David A.
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Optical tomography -- Methods ,Image processing -- Technology application ,Estimation theory -- Research ,Statistical methods -- Usage ,Diagnostic imaging -- Research ,Technology application ,Business ,Electronics ,Electronics and electrical industries ,Health care industry - Abstract
Voxel-based reconstructions in diffuse optical tomography (DOT) using a quadratic regularization functional tend to produce very smooth images due to the attenuation of high spatial frequencies. This then causes difficulty in estimating the spatial extent and contrast of anomalous regions such as tumors. Given an assumption that the target image is piecewise constant, we can employ a parametric model to estimate the boundaries and contrast of an inhomogeneity directly. In this paper, we describe a method to directly reconstruct such a shape boundary from diffuse optical measurements. We parameterized the object boundary using a spherical harmonic basis, and derived a method to compute sensitivities of measurements with respect to shape parameters. We introduced a centroid constraint to ensure uniqueness of the combined shape/center parameter estimate, and a projected Newton method was utilized to optimize the object center position and shape parameters simultaneously. Using the shape Jacobian, we also computed the Cramer-Rao lower bound on the theoretical estimator accuracy given a particular measurement configuration, object shape, and level of measurement noise. Knowledge of the shape sensitivity matrix and of the measurement noise variance allows us to visualize the shape uncertainty region in three dimensions, giving a confidence region for our shape estimate. We have implemented our shape reconstruction method, using a finite-difference-based forward model to compute the forward and ad-joint fields. Reconstruction results are shown for a number of simulated target shapes, and we investigate the problem of model order selection using realistic levels of measurement noise. Assuming a signal-to-noise ratio in the amplitude measurements of 40 dB and a standard deviation in the phase measurements of 0.1[degrees], we are able to estimate an object represented with an eighth-order spherical harmonic model having an absorption contrast of 0.15 [cm.sup.-1] and a volume of 4.82 [cm.sup.3] with errors of less than 10% in object volume and absorption contrast. We also investigate the robustness of our shape-based reconstruction approach to a violation of the assumption that the medium is purely piecewise constant. Index Terms--optical tomography, image reconstruction, inverse problems, shape-based imaging.
- Published
- 2008
5. Subsurface sensing under sensor positional uncertainty
- Author
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Tarokh, Ashley B. and Miller, Eric L.
- Subjects
Sensors -- Analysis ,Induction, Electromagnetic -- Analysis ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
We consider the problem of classifying buried objects using electromagnetic induction data collected in a setting where there are errors in sensor positioning. Using a series of decay constants (or equivalently, Laplace plane poles) as features for classification, our algorithm seeks to estimate these poles and, subsequently, to determine the type of object in the sensor field of view. In many practical scenarios, a set of data is often accompanied by domain knowledge that the location of the transmitters and/or receivers is only known to within some degree of accuracy (e.g., 10 cm in the along-track direction and 5-cm cross-track). Here, we develop an approach to the extraction of information from such data sets in which the quantitative positional bound information is used in the context of a min--max optimization strategy. Specifically, we look for the parameters of interest that minimize the maximum data residual, where the maximum error is computed over ellipsoids or polyhedra of possible sensor locations defined by the bound information. Our formulation admits data collection with independent or dependent positional uncertainty values at successive nominal collection locations. Our algorithms for solving this optimization problem are validated using simulated and measured data. Index Terms--Bounded-data uncertainty, dynamic programming, electromagnetic induction (EMI), min--max optimization, object classification, parameter estimation, positional uncertainty, subsurface sensing.
- Published
- 2007
6. Subsurface sensing of buried objects under a randomly rough surface using scattered electromagnetic field data
- Author
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Firoozabadi, Reza, Miller, Eric L., Rappaport, Carey M., and Morgenthaler, Ann W.
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Electromagnetic fields -- Research ,Ground penetrating radar -- Research ,Remote sensing -- Research ,Underground areas -- Research ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
This paper proposes a new inverse method for microwave-based subsurface sensing of lossy dielectric objects embedded in a dispersive lossy ground with an unknown rough surface. An iterative inversion algorithm is employed to reconstruct the geometry and dielectric properties of the half-space ground as well as that of the buried object. B-splines are used to model the shape of the object as well as the height of the rough surface. In both cases, the control points for the spline function represent the unknowns to be recovered. A single-pole rational transfer function is used to capture the dispersive nature of the background. Here, the coefficients in the numerator and denominator are the unknowns. The approach presented in this paper is based on the state-of-the-art semianalytic mode matching forward model, which is a fast and efficient algorithm to determine the scattered electromagnetic fields. Numerical experiments involving two-dimensional geometries and TM incident plane waves demonstrate the accuracy and reliability of this inverse method. Index Terms--B-splines, dispersive media, ground-penetrating radar (GPR), inversion methods, nonlinear optimization, rough surface, subsurface sensing.
- Published
- 2007
7. Spherical harmonics microwave algorithm for shape and location reconstruction of breast cancer tumor
- Author
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El-Shenawee, Magda and Miller, Eric L.
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Algorithms -- Usage ,Breast cancer -- Health aspects ,Breast tumors -- Care and treatment ,Algorithm ,Business ,Electronics ,Electronics and electrical industries ,Health care industry - Abstract
A reconstruction algorithm to simultaneously estimate the shape and location of three-dimensional breast cancer tumor is presented and its utility is analyzed. The approach is based on a spherical harmonic decomposition to capture the shape of the tumor. We combine a gradient descent optimization method with a direct electromagnetic solver to determine the coefficients in the harmonic expansion as well as the coordinates of the center of the tumor. The results demonstrate the potential advantage of collecting data using a multiple-view/tomographic-type strategy. We show how the order of the harmonic expansion must be increased to capture increasingly 'irregularly' shaped tumors and explore the resulting increase in the central processing unit (CPU) time required by the algorithm. Our approach shows accurate reconstruction of the tumor image regardless of the source polarization. This work demonstrates the promise of the algorithm when used on data corrupted with Gaussian noise and when perfect knowledge of the tumor electrical properties is not available. Index Terms--Breast cancer imaging, electromagnetic scattering, shape reconstruction problems, steepest descent gradient method.
- Published
- 2006
8. Multiple-incidence and multifrequency for profile reconstruction of random rough surfaces using the 3-D electromagnetic fast multipole model
- Author
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El-Shenawee, Magda and Miller, Eric L.
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Remote sensing -- Research ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
A fast algorithm for reconstructing the profile of random rough surfaces using electromagnetic scattering data is presented. The algorithm is based on merging a fast forward solver and an efficient optimization technique. The steepest descent fast multipole method is used as the three-dimensional fast forward solver. A rapidly convergent descent method employing a 'marching-on' strategy for processing multifrequency and multi-incidence angle data is introduced to minimize an underlying cost function. The cost function represents the error between true (synthetic) and simulated scattered field data. Several key issues that impact the accuracy in reconstructing the rough profile are examined in this work, e.g., the location and number of receivers, the incident and scattered directions, the surface roughness, and details regarding the manner in which sensitivity information is computed in the inversion scheme. The results show that using the multiple-incidence (one angle at a time) and the multifrequency (one frequency at a time) strategies lead to improve the profile reconstruction. Index Terms--Fast algorithms, inverse scattering, optimization techniques, profile reconstruction, rough surface scattering.
- Published
- 2004
9. Minimum entropy regularization in frequency-wavenumber migration to localize subsurface objects
- Author
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Xu, Xiaoyin, Miller, Eric L., and Rappaport, Carey M.
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Ground penetrating radar -- Research ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
Optimized versions of frequency-wavenumber (F-K) migration methods are introduced to better focus ground-penetrating radar {{;PR} data in applications of shallow subsurface object localization, e.g., landmine remediation. Migration methods are based on the wave equation and operate by backpropagating the received data into the earth so as to localize buried objects. Traditional F-K migration is based on an underlying assumption that the wavefields propagate in a homogeneous medium. The presence of a rough air-ground interface in the GPR case degrades the localization ability. To overcome this problem in the context of the F-K algorithm, we introduce lateral variations in the velocity of waves in the medium. An optimization approach is employed to choose that velocity function that results in a well-focused image where an entropy-like criterion is used to quantify the notion of focus. Extension of the basic method to lossy medium is also described. The utility of these techniques is demonstrated using field data from a number of GPR systems. Index Terms--Complex-velocity frequency-wavenumber (F-K) migration, dispersive medium, frequency-wavenumber (F-K) migration, minimum entropy optimization, Tikhonov regularization, varimax norm.
- Published
- 2003
10. Template matching based object recognition with unknown geometric parameters
- Author
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Dufour, Roger M., Miller, Eric L., and Galatsanos, Nikolas P.
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Image processing -- Research ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
The problem of how to locate an object in an image when size and rotation are not known is investigated and discussed.
- Published
- 2002
11. A doubly adaptive approach to dynamic MRI sequence estimation
- Author
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Hoge, William Scott, Miller, Eric L., Lev-Ari, Hanoch, Brooks, Dana H., and Panych, Lawrence P.
- Subjects
Magnetic resonance imaging -- Research ,Image processing -- Research ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
An approach is proposed for estimating dynamic magnetic resonance imaging (MRI) sequences. The approach is based on two complimentary strategies: an adaptive framework for estimating the MRI images themselves; and an adaptive technique for tailoring MRI system excitations for each data acquisition.
- Published
- 2002
12. Statistical method to detect subsurface objects using array ground-penetrating radar data
- Author
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Xu, Xiaoyin, Miller, Eric L., Rappaport, Carey M., and Sower, Gary D.
- Subjects
Ground penetrating radar -- Usage ,Geological research -- Equipment and supplies ,Signal processing -- Methods ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
We introduce a combination of high-dimensional analysis of variance (HANOVA) and sequential probability ratio test (SPRT) to detect buried objects from an array ground-penetrating radar (GPR) surveying a region of interest in a progressive manner. Using HANOVA, we exploit the transient characteristic of GPR signals in the time domain to extract information about buried objects at fixed positions of the array. Based on the output of the HANOVA, the SPRT is employed to make detection decisions recursively as the array moves downtrack. The method is on-line implementable and of low computational complexity. Our approach is validated using field-data from two quite different GPR sensing systems designed for landmine detection applications. Index Terms--Analysis of variance (ANOVA), array signal processing, GPR mine detection, sequential detection, transient signal analysis.
- Published
- 2002
13. Optimum PML, ABC conductivity profile in FDFD
- Author
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Marengo, Edwin A., Rappaport, Carey M., and Miller, Eric L.
- Subjects
Electrical conductivity -- Research ,Boundary value problems -- Research ,Business ,Electronics ,Electronics and electrical industries - Abstract
We address the problem of optimally choosing the conductivity ([Sigma]) profile of a perfectly-matched-layer absorbing boundary condition (PML ABC) with prescribed number of layers so as to minimize reflections for a wide range of incidence angles and for a narrow (CW) or broad frequency-band. A new one-dimensional (1-D), frequency-domain description of 2-D PML performance is developed, validated and used in PML-[Sigma] profile optimization. An exhaustive search for PML-[Sigma] profiles that minimize reflections over a prescribed wide angle-range is carried out. Our procedure yields PML-[Sigma] profiles with better performance than previously reported values, for given number of layers. Index Terms - Perfectly-matched-layer (PML).
- Published
- 1999
14. Efficient computational methods for wavelet domain signal restoration problems
- Author
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Miller, Eric L.
- Subjects
Algorithms -- Research ,Signal processing -- Research ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
An efficient, wavelet domain algorithm for the calculation of error variance information for a wide class of linear inverse problems posed in a maximum a posteriori estimation framework has been developed. The algorithm is based on the permutation and partitioning of the Fisher information matrix to maximize a diagonal dominance criterion. It was shown that this diagonal approximation allows for the accurate recovery of the error variances.
- Published
- 1999
15. Recursive T-matrix methods for scattering from multiple dielectric and metallic objects
- Author
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Sahin, Adnan and Miller, Eric L.
- Subjects
Scattering (Physics) -- Methods ,Dielectrics -- Analysis ,Algorithms -- Usage ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
We present an efficient, stable, recursive T-matrix algorithm to calculate the scattered field from a heterogeneous collection of spatially separated objects. The algorithm is based on the use of higher order multipole expansions than those typically employed in recursive T-matrix techniques. The use of these expansions introduces instability in the recursions developed in [5] and [6], specifically in the case of near-field computations. By modifying the original recursive algorithm to avoid these instabilities, we arrive at a flexible and efficient forward solver appropriate for a variety of scattering calculations. The algorithm can be applied when the objects are dielectric, metallic, or a mixture of both. We verify this method for cases where the scatterers are electrically small (fraction of a wavelength) or relatively large (12[Lambda]). While developed for near-field calculation, this approach is applicable for far-field problems as well. Finally, we demonstrate that the computational complexity of this approach compares favorably with comparable recursive algorithms. Index Terms - Elecromagnetic scattering.
- Published
- 1998
16. A multiscale, statistically based inversion scheme for linearized inverse scattering problems
- Author
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Miller, Eric L. and Willsky, Alan S.
- Subjects
Scattering (Mathematics) -- Usage ,Signal processing -- Analysis ,Stochastic processes -- Models ,Analysis of covariance -- Usage ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
The application of multiscale and stochastic techniques to the solution of a linearized inverse scattering problem is presented. This approach allows for the explicit and easy handling of many difficulties associated with problems of this type. Regularization is accomplished via the use of a multiscale prior stochastic model which offers considerable flexibility for the incorporation of prior knowledge and constraints. We use the relative error covariance matrix (RECM), introduced in [28], as a tool for quantitatively evaluating the manner in which data contribute to the structure of a reconstruction. Given a set of scattering experiments, the RECM is used for understanding and analyzing the process of data fusion and allows us to define the space-varying optimal scale for reconstruction as a function of the nature (resolution, quality, and distribution of observation points) of the available measurement sets. Examples of our multiscale inversion algorithm are presented using the Born approximation of an inverse electrical conductivity problem formulated so as to illustrate many of the features associated with inverse scattering problems arising in fields such as geophysical prospecting and medical imaging.
- Published
- 1996
17. Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images
- Author
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Xiaoyin Xu, Sarhadi, Mansoor, Dongbin Chen, and Miller, Eric L.
- Subjects
Image processing -- Analysis ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
An adaptive two-pass rank order filter to remove impulse noises in highly corrupt images is presented. The working process of this adaptive filter based on the standard median filter, center-weighted median filter (CWMF), adaptive weighted median filter, lower-upper-middle (LUM) filter and soft-decision rank-order-mean (SD-ROM) filter, by analyzing the spatial distribution of estimated impulse noise is discussed.
- Published
- 2004
18. Three-Dimensional Subsurface Analysis of Electromagnetic Scattering from Penetrable/PEC Objects Buried Under Rough Surfaces: Use of the Steepest Descent Fast Multipole Method
- Author
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El-Shenawee, Magda, Rappaport, Carey, Miller, Eric L., and Silevitch, Michael B.
- Subjects
Remote sensing -- Methods ,Electromagnetism -- Usage ,Mines, Military -- Management ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
The electromagnetic scattering from a three-dimensional (3-D) shallow object buried under a two-dimensional (2-D) random rough dielectric surface is analyzed in this work. The buried object can be a perfect electric conductor (PEC) or can be a penetrable dielectric with size and burial depth comparable to the free-space wavelength. The random rough ground surface is characterized with Gaussian statistics for surface height and for surface autocorrelation function. The Poggio, Miller, Chang, Harrington, and Wu (PMCHW) integral equations are implemented and extended in this work. The integral equation-based steepest descent fast multipole method (SDFMM), that was originally developed at UIUC, has been used and the computer code based on this algorithm has been successfully modified to handle the current application. The significant potential of the SDFMM code is that it calculates the unknown moment method surface electric and magnetic currents on the scatterer in a dramatically fast, efficient, and accurate manner. Interactions between the rough surface interface and the buried object are fully taken into account with this new formulation. Ten incident Gaussian beams with the same elevation angle and different azimuth angles are generated for excitation as one possible way of having multiple views of a given target. The scattered electric fields due to these ten incident beams are calculated in the near zone and their complex vector average over the multiple views is computed. The target signature is obtained by subtracting the electric fields scattered from the rough ground only from those scattered from the ground with the buried anti-personnel mine. Significant polarization dependency is observed for the PEC object signature compared with that of the penetrable object, which can be used in target discrimination. Moreover, fields scattered above the rough ground experience significantly more distortion than fields transmitted into the ground. Index Terms--Buried-target, computational electromagnetics, fast multipole method (FMM), mine detection, rough surface scattering.
- Published
- 2001
19. Object Detection Using High Resolution Near-Field Array Processing
- Author
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Sahin, Adnan and Miller, Eric L.
- Subjects
Ground penetrating radar -- Methods ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
In this paper, we present an algorithm for the detection and localization of an unknown number of objects buried in a halfspace and present in the near field of a linear receiver array. To overcome the nonplanar nature of the wavefield over the array, the full array is divided into a collection of subarrays such that the scattered fields from objects are locally planar at each subarray. Using the multiple signal classification (MUSIC) algorithm, directions of arrival (DOA) of locally planar waves at each subarray are found. By triangulating these DOA's, a set of crossings, condensed around expected object locations, are obtained. To process this spatial crossing pattern, we develop a statistical model for the distribution of these crossings and employ hypotheses testing techniques to identify a collection of small windows likely to contain targets. Finally, the results of the hypothesis tests are used to estimate the number and locations of the targets. Using simulated data, we demonstrate the usefulness and performance of this approach for typical background electrical properties and signal to noise ratios. Index Terms--Array processing, hypothesis testing, subsurface object detection.
- Published
- 2001
20. A New Shape-Based Method for Object Localization and Characterization from Scattered Field Data
- Author
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Miller, Eric L., Kilmer, Misha, and Rappaport, Carey
- Subjects
Nonlinear theories -- Usage ,Polynomials -- Usage ,Scattering (Physics) -- Analysis ,Image processing -- Methods ,Business ,Earth sciences ,Electronics and electrical industries - Abstract
The problem of characterizing the geometric structure of an object buried in an inhomogeneous halfspace of unknown composition is considered. We develop a nonlinear inverse scattering algorithm based on a low-dimensional parameterization of the unknown object and the background. In particular, we use a low-order polynomial expansion to represent the spatial variations in the real and imaginary parts of the object and background complex permittivities. The boundary separating the target from the unknown background is described using a periodic, quadratic B-spline curve whose control points can be individually manipulated. We determine the unknown control point locations and contrast expansion coefficients using a greedy-type approach to minimize a regularized least-squares cost function. The regularizer used here is designed to constrain the geometric structure of the boundary of the object and is closely related to snake methods employed in the image processing community. We demonstrate the performance of our approach via extensive numerical simulation involving two-dimensional (2-D), [TM.sub.z] scattering geometries. Our results indicate a strong ability to localize and estimate the shape of the object even in the presence of an unknown and inhomogeneous background. Index Terms--B-splines, clutter models, inverse scattering, mine detection, shape-based methods.
- Published
- 2000
21. Inverse perspective transformation for video surveillance
- Author
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Schouten, Theo E., van den Broek, Egon, Bouman, Charles A., Miller, Eric L., and Pollak, Ilya
- Subjects
HMI-CI: Computational Intelligence ,HMI-VRG: Virtual Reality and Graphics ,Fast Exact Euclidean Distance (FEED) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Inverse perspective transformation ,Inverse ,IR-58739 ,Image (mathematics) ,Fast Exact ,Camera auto-calibration ,Computer graphics (images) ,video surveillance ,Computer vision ,Mathematics ,business.industry ,Euclidean Distance (FEED) ,EWI-21110 ,Frame (networking) ,Perspective transformation ,Euclidean distance ,METIS-252709 ,Transformation (function) ,distance maps/transforms ,Video tracking ,Artificial intelligence ,business - Abstract
In this research, we are considering the use of the inverse perspective transformation in video surveillance applications that observe (and possible influence) scenes consisting of moving and stationary objects; e.g., people on a parking area. In previous research, objects were detected on video streams and identified as moving or stationary. Subsequently, distance maps were generated by the Fast Exact Euclidean Distance (FEED) transformation, which uses frame-to-frame information to generate distance maps for video frames in a fast manner. From the resulting distance maps, different kinds of surveillance parameters can be derived. The camera was placed above the scene, and hence, no inverse perspective transformation was needed. In this work,the case is considered the case that the camera is placed under an arbitrary angle on the side of the scene, which might be a more feasible placement than on the top. It will be shown that an image taken from a camera on the side can be easily and fast converted to an image as would be taken by a camera on the top. The allows the use of the previously developed methods after converting each frame of a video stream or only objects of interest detected on them.
- Published
- 2008
22. An efficient region of interest acquisition method for dynamic magnetic resonance imaging
- Author
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Hoge, William Scott, Miller, Eric L., Lev-Ari, Hanoch, Brooks, Dana H., Karl, William Clem, and Panych, Lawrence P.
- Subjects
Magnetic resonance imaging -- Research ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
Research into the problem of reduced-order magnetic resonance imaging (MRI) acquisition is presented. It was possible to develop a problem formulation, linked analysis, and a computational technique to identify a set of excitation and reconstruction vectors for efficient MRI acquisition of an arbitrarily shaped region of interest.
- Published
- 2001
23. Statistically based methods for anomaly characterization in images from observations of scattered radiation
- Author
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Miller, Eric L.
- Subjects
Image processing -- Research ,Scattering, Radiation -- Research ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
An algorithm for the detection, localization and characterization of anomalous structures in a region that has scattered radiation along its boundary was developed based on a full nonlinear scattering model. Part of the algorithm is a method for incorporating prior geometric information associated to the anomaly detection problem. Experimental results showed that the algorithm is capable of obtaining high detection and low false alarm rates even in very noisy environments.
- Published
- 1999
24. Photometry in UV astronomical images of extended sources in crowded field using deblended images in optical visible bands as Bayesian priors
- Author
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Antoine Llebaria, Bruno Milliard, D. Vibert, Stephane Arnouts, Simon Conseil, M. Zamojski, Mireille Guillaume, Bouman, Charles A., Miller, Eric L., and Pollak, Ilya
- Subjects
Physics ,Photon ,Galactic astronomy ,business.industry ,media_common.quotation_subject ,Astrophysics::Instrumentation and Methods for Astrophysics ,Field of view ,Astrophysics::Cosmology and Extragalactic Astrophysics ,Astrophysics ,Sensor fusion ,Galaxy ,Photometry (optics) ,Stars ,Optics ,Sky ,business ,Astrophysics::Galaxy Astrophysics ,media_common - Abstract
Photometry of astrophysical sources, galaxies and stars, in crowded field images, if an old problem, is still a challenging goal, as new space survey missions are launched, releasing new data with increased sensibility, resolution and field of view. The GALEX mission, observes in two UV bands and produces deep sky images of millions of galaxies or stars mixed together. These UV observations are of lower resolution than same field observed in visible bands, and with a very faint signal, at the level of the photon noise for a substantial fraction of objects. Our purpose is to use the better known optical counterparts as prior information in a Bayesian approach to deduce the UV flux. Photometry of extended sources has been addressed several times using various techniques: background determination via sigma clipping, adaptative-aperture, point-spread-function photometry, isophotal photometry, to lists some. The Bayesian approach of using optical priors for solving the UV photometry has already been applied by our team in a previous work. Here we describe the improvement of using the extended shape inferred by deblending the high resolution optical images and not only the position of the optical sources. The resulting photometric accuracy has been tested with simulation of crowded UV fields added on top of real UV images. Finally, this helps to converge to smaller and flat residual and increase the faint source detection threshold. It thus gives the opportunity to work on 2nd order effects, like improving the knowledge of the background or point-spread function by iterating on them.
- Published
- 2009
25. Multi-object segmentation using coupled nonparametric shape and relative pose priors
- Author
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Gozde Unal, Zeynep Firat, Aytül Erçil, Devrim Unay, Ahmet Ekin, Mujdat Cetin, Octavian Soldea, Mustafa Gökhan Uzunbas, Bouman, Charles A., Miller, Eric L., and Pollak, Ilya
- Subjects
Computer science ,business.industry ,Segmentation-based object categorization ,Kernel density estimation ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Scale-space segmentation ,Pattern recognition ,Image segmentation ,Edge detection ,TK Electrical engineering. Electronics Nuclear engineering ,Kernel method ,Computer Science::Computer Vision and Pattern Recognition ,Maximum a posteriori estimation ,Computer vision ,Segmentation ,Artificial intelligence ,business - Abstract
We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.
- Published
- 2009
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26. The smashed filter for compressive classification and target recognition
- Author
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Richard G. Baraniuk, Dharmpal Takhar, Marco F. Duarte, Mark A. Davenport, Michael B. Wakin, Jason N. Laska, Kevin F. Kelly, Bouman, Charles A., Miller, Eric L., and Pollak, Ilya
- Subjects
Pixel ,Contextual image classification ,business.industry ,Matched filter ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern recognition ,Scale (descriptive set theory) ,Iterative reconstruction ,Filter (signal processing) ,Compressed sensing ,Pattern recognition (psychology) ,Artificial intelligence ,business ,Mathematics - Abstract
The theory of compressive sensing (CS) enables the reconstruction of a sparse or compressible image or signal from a small set of linear, non-adaptive (even random) projections. However, in many applications, including object and target recognition, we are ultimately interested in making a decision about an image rather than computing a reconstruction. We propose here a framework for compressive classification that operates directly on the compressive measurements without first reconstructing the image. We dub the resulting dimensionally reduced matched filter the smashed filter. The first part of the theory maps traditional maximum likelihood hypothesis testing into the compressive domain; we find that the number of measurements required for a given classification performance level does not depend on the sparsity or compressibility of the images but only on the noise level. The second part of the theory applies the generalized maximum likelihood method to deal with unknown transformations such as the translation, scale, or viewing angle of a target object. We exploit the fact the set of transformed images forms a low-dimensional, nonlinear manifold in the high-dimensional image space. We find that the number of measurements required for a given classification performance level grows linearly in the dimensionality of the manifold but only logarithmically in the number of pixels/samples and image classes. Using both simulations and measurements from a new single-pixel compressive camera, we demonstrate the effectiveness of the smashed filter for target classification using very few measurements.
- Published
- 2007
27. Seismic image reconstruction using complex wavelets
- Author
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Richard Hobbs, Nick Kingsbury, Mark A. Miller, Bouman, Charles A., and Miller, Eric L.
- Subjects
Discrete wavelet transform ,Geophysical imaging ,business.industry ,Wavelet transform ,Iterative reconstruction ,Physics::Geophysics ,Wavelet ,Inverse scattering problem ,Computer vision ,Artificial intelligence ,Complex wavelet transform ,business ,Geology ,Image restoration ,Remote sensing - Abstract
The aim of seismic imaging is to reconstruct subsurface reflectivity from scattered acoustic data. In standard reconstruction techniques, the reflectivity model parameters are usually defined as a grid of point scatterers over the area or volume of the subsurface to be imaged. We propose an approach to subsurface imaging using the Dual Tree Complex Wavelet Transform (DT-CWT) as a basis for the reflectivity. This basis is used in conjunction with an iterative optimization which frames the problem as a linearized inverse scattering problem. We demonstrate the method on synthetic data and a marine seismic data set acquired over the Gippsland Basin near Australia. The technique is shown to reduce noise and processing artifacts while preserving discontinuities. It is likely to be particularly useful in cases where the acquired date is incomplete.
- Published
- 2005
Catalog
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