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Conference on algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII

作者： Johnson, Steven E. Eismann, Michael T. AF Inst Technol Wright Patterson AFB OH USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9780819466877

An algorithm called the constrained signal detector (CSD) was recently introduced for the purpose of target detection in hyperspectral images. The CSD assumes that hyperspectral pixels can be modeled as linear mixtures of material signatures and stochastic noise. In theory, the CSD is superior to the popular orthogonal subspace projection (OSP) technique. The CSD requires knowledge of the spectra of the background materials in a hyperspectral image. But in practice the background material spectra are often unknown due to uncertainties in illumination, atmospheric conditions, and the composition of the scene being imaged. In this paper, estimation techniques are used to create an adaptive version of the CSD. This adaptive algorithm uses training data to develop a description of the image background and adaptively implement the CSD. The adaptive CSD only requires knowledge of the target spectrum. It is shown through simulations that the adaptive CSD performs nearly as well as the CSD operating with complete knowledge of the background material spectra. The adaptive CSD is also tested using real hyperspectral image data and its performance is compared to OSP.

关键词： Signal detection Sensors Hyperspectral imaging Target detection Detection and tracking algorithms Hyperspectral target detection Signal to noise ratio Algorithm development expectation maximization algorithms Reflectivity

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Spline-regularized ordered subsets expectation-maximization algorithms for emission computed tomography

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JOURNAL OF ELECTRONIC IMAGING 2003年第1期12卷 17-24页

作者： Lee, SJ Paichai Univ Dept Elect Engn Taejon 302735 South Korea

We describe ordered subsets (OS) algorithms applied to regularized expectation-maximization (EM) algorithms for emission tomography. Our reconstruction algorithms are based on a maximum a posteriori approach, which allows us to incorporate a priori information in the form of a regularizer to stabilize the unstable EM algorithm. In this work, we use two-dimensional smoothing splines as regularizers. Our motivation for using such regularizers stems from the fact that, by relaxing the requirement of imposing significant spatial discontinuities and using instead quadratic smoothing splines, solutions are easier to compute and hyperparameter calculation becomes less of a problem. To optimize our objective function, we use the method of iterated conditional modes, which is useful for obtaining convenient closed-form solutions. In this case, step sizes or line-search algorithms necessary for gradient-based descent methods are also avoided. We finally accelerate the resulting algorithm using the OS principle and propose a principled way of scaling smoothing parameters to retain the strength of smoothing for different subset numbers. Our experimental results indicate that our new methods provide quantitatively robust results as well as a considerable acceleration. (C) 2003 SPIE and IST.

关键词： expectation maximization algorithms Reconstruction algorithms Algorithm development Computed tomography Standards development Tin Tomography Mathematical modeling Sensors Positron emission tomography

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Unsupervised texture segmentation using a nonlinear energy optimization method

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JOURNAL OF ELECTRONIC IMAGING 2006年第3期15卷 033006.1-033006.8页

作者： Mahmoodi, Sasan Sharif, Bayan S. Univ Newcastle Sch Biol & Psychol Psychol Div Newcastle Upon Tyne NE2 4HH Tyne & Wear England Univ Newcastle Sch Elect Elect & Comp Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

A nonlinear functional is considered for segmentation of images containing structural textures. A structural texture pattern in an image is characterized by a certain amplitude spectrum, and segmentation of different patterns is obtained by detecting different regions with different amplitude spectra. A gradient-descent-based algorithm is proposed by deriving equations minimizing the functional. This algorithm, implementing the solutions minimizing the functional, is based on the level set method. An effective method employed in this algorithm is shown to be robust in a noisy environment. Experimental results demonstrate that the proposed method outperforms segmentation obtained by using the simulated annealing algorithm based on Gaussian Markov random fields. (c) 2006 SPIE and IS&T.

关键词： Image segmentation Image processing algorithms and systems Multiplexers expectation maximization algorithms Detection and tracking algorithms Image filtering Reconstruction algorithms algorithms Smoothing Spatial frequencies

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Ultra-thin multiple-channel LWIR imaging systems

Ultra-thin multiple-channel LWIR imaging systems

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Conference on Infrared and Photoelectronic Imagers and Detector Devices

作者： Shankar, M. Willett, R. Pitsianis, N. P. Kolste, R. Te Chen, C. Gibbons, R. Brady, D. J. Duke Univ Fitzpatrick Inst Photon Durham NC 27708 USA Digital Opt Corp Charlotte NC 28262 USA Univ Delaware Newark DE 19716 USA Raytheon Co Mc Kinney TX 75071 USA

ISBN: (纸本)0819463736

Infrared camera systems may be made dramatically smaller by simultaneously collecting several low-resolution images with multiple narrow aperture lenses rather than collecting a single high-resolution image with one wide aperture lens. Conventional imaging systems consist of one or more optical elements that image a scene on the focal plane. The resolution depends on the wavelength of operation and the f-number of the lens system, assuming a diffraction limited operation. An image of comparable resolution may be obtained by using a multi-channel camera that collects multiple low-resolution measurements of the scene and then reconstructing a high-resolution image. The proposed infrared sensing system uses a three-by-three lenslet array with an effective focal length of 1.9mm. and overall system length of 2.3mm, and we achieve image resolution comparable to a conventional single lens system having a focal length of 5.7mm and overall system length of 26mm. The high-resolution final image generated by this system is reconstructed from the noisy low-resolution images corresponding to each lenslet;this is accomplished using a computational process known as superresolution reconstruction. The novelty of our approach to the superresolution problem is the use of wavelets and related multiresolution method within a expectation-maximization framework to improve the accuracy and visual quality of the reconstructed image. The wavelet-based regularization reduces the appearance of artifacts while preserving key features such as edges and singularities. The processing method is very fast, making the integrated sensing and processing viable for both time-sensitive applications and massive collections of sensor outputs.

关键词： Imaging systems Cameras Super resolution Infrared cameras Image processing expectation maximization algorithms Image resolution Sensors Long wavelength infrared Microlens array

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A theory of PHD filters of higher order in target number

A theory of PHD filters of higher order in target number

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XV

作者： Mahler, Ronald Lockheed Martin MS Tact Syst Eagan MN USA

ISBN: (纸本)0819462918

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In this paper we show that this and much more is possible. We derive a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number.

关键词： Digital filtering Filtering (signal processing) Sensors Nonlinear filtering Target detection Electronic filtering Electroactive polymers expectation maximization algorithms Motion models Signal processing

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An iterative, wavelet-based deconvolution algorithm for the restoration of ultrasound images in an EM framework

An iterative, wavelet-based deconvolution algorithm for the ...

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Medical Imaging 2006 Conference

作者： Ng, J. K. H. Prager, R. W. Kingsbury, N. G. Treece, G. M. Gee, A. H. Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England

ISBN: (纸本)0819461903

The quality of medical ultrasound images is limited by inherent poor resolution due to the finite temporal bandwidth of the acoustic pulse and the non-negligible width of the system point-spread function. One of the major difficulties in designing a practical and effective restoration algorithm is to develop a model for the tissue reflectivity that can adequately capture significant image features without being computationally prohibitive. The reflectivities of biological tissues do not exhibit the piecewise smooth characteristics of natural images considered in the standard image processing literature;while the macroscopic variations in echogenicity are indeed piecewise smooth, the presence of sub-wavelength scatterers adds a pseudo-random component at the microscopic level. This observation leads us to propose modelling the tissue reflectivity as the product of a piecewise smooth echogenicity map and a unit-variance random field. The chief advantage of such an explicit representation is that it allows us to exploit representations for piecewise smooth functions (such as wavelet bases) in modelling variations in echogenicity without neglecting the microscopic pseudo-random detail. As an example of how this multiplicative model may be exploited, we propose an expectation-maximisation (EM) restoration algorithm that alternates between inverse filtering (to estimate the tissue reflectivity) and logarithmic wavelet denoising (to estimate the echogenicity map). We provide simulation and in vitro results to demonstrate that our proposed algorithm yields solutions that enjoy higher resolution, better contrast and greater fidelity to the tissue reflectivity compared with the current state-of-the-art in ultrasound image restoration.

关键词： expectation maximization algorithms Reflectivity Tissues Ultrasonography Deconvolution Algorithm development Image resolution Medical imaging Modeling Wavelets

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Phase-polarization control as a route to plasmonic nano-devices

Phase-polarization control as a route to plasmonic nano-devi...

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Conference on Physics and Simulation of Optoelectronic Devices XIV

作者： Sukharev, Maxim Seideman, Tamar Northwestern Univ Dept Chem Evanston IL 60208 USA

ISBN: (纸本)0819461571

Several concepts of coherent control are extended to manipulate light propagating along metal nano-particle arrays. A phase-polarization control strategy is proposed and applied to control the electromagnetic energy transport via nano-array constructs with multiple branching intersections, leading to an optical switch or inverter far below the diffraction limit. An optimal control approach, based on the genetic algorithm optimization procedure, is next generalized to suggest a systematic design tool for plasmonic nano-devices, where both material properties of nano-arrays and incident field parameters are optimized in order to make devices with desired functionality. The proposed schemes axe also used to better understand the physics underlying the phenomenon of electromagnetic energy transport via metal nano-constructs. Several applications of the phase-polarization and optimal control strategies are considered.

关键词： Particles Metals Control systems Plasmonics Sensors Electromagnetism Plasmons expectation maximization algorithms Polarization Physics

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Probabilistic nodule filtering in thoracic CT scans

Probabilistic nodule filtering in thoracic CT scans

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Medical Imaging 2006 Conference

作者： Wu, Changhua Agam, Cady Kettering Univ Dept Sci & Math Flint MI 48504 USA IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)0819464236

Automated detection of lung nodules in thoracic CT scans is an important clinical challenge. Blood vessels form a major source of false positives in automated nodule detection systems. Hence, the performance of such systems may be improved by enhancing nodules while suppressing blood vessels. Ideally, nodule enhancement filters should enhance nodules while suppressing vessels and lung tissue. A distinction between vessels and nodules is normally obtained through eigenvalue analysis of the Hessian matrix. The Hessian matrix is a second order differential quantity and so is sensitive to noise. Furthermore, by relying on principal curvatures alone, existing filters are incapable of distinguishing between nodules and vessel junctions, and are incapable of handling cases in which nodules touch vessels. In this paper we develop novel nodule enhancement filters that are capable of suppressing junctions and are capable of handling cases in which nodules appear to touch or even overlap with vessels. The proposed filters are based on optimized probabilistic models derived from eigenvalue analysis of the gradient correlation matrix which is a first order differential quantity and so are less sensitive to noise compared with known vessel enhancement filters. The proposed filters are evaluated and compared to known techniques both qualitatively, quantitatively. The evaluation includes both synthetic and actual clinical data.

关键词： Blood vessels Image segmentation Computed tomography Lung expectation maximization algorithms Lithium Gaussian filters Image enhancement Image filtering Local area networks

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A fast algorithm for 3-D reconstruction from unoriented projections and cryo electron microscopy of viruses

A fast algorithm for 3-D reconstruction from unoriented proj...

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Conference on Computational Imaging IV

作者： Lee, Junghoon Zheng, Yili Doerschuk, Peter C. Purdue Univ Sch Elect & Comp Engn 465 Northwestern Ave W Lafayette IN 47907 USA

ISBN: (纸本)0819461059

In a cryo electron microscopy experiment, the data is noisy 2-D projection images of the 3-D electron scattering intensity where the orientation of the projections is not known. In previous work we have developed a solution for this problem based on a maximum likelihood estimator that is computed by an expectation maximization algorithm. In the expectation maximization algorithm the expensive step is the expectation which requires numerical evaluation of 3- or 5-dimensional integrations of a square matrix of dimension equal to the number of Fourier series coefficients used to describe the 3-D reconstruction. By taking advantage of the rotational properties of spherical harmonics, we can reduce the integrations of a matrix to integrations of a scalar. The key properties is that a rotated spherical harmonic can be expressed as a linear combination of the other harmonics of the same order and that the weights in the linear combination factor so that each of the three factors is a function of only one of the Euler angles describing the orientation of the projection.

关键词： expectation maximization algorithms Fourier transforms Electron microscopy Reconstruction algorithms Viruses 3D image processing Particles Radon Scattering Spherical lenses

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Johnson distribution models of hyperspectral image data clusters

Johnson distribution models of hyperspectral image data clus...

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Conference on algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII

作者： Meidunas, Eduardo C. Gustafson, Steven C. USAF Inst Technol Dept Elect & Comp Engn Wright Patterson AFB OH 45433 USA

ISBN: (纸本)0819462896

The Johnson System for characterizing an empirical distribution is used to model the non-normal behavior of Mahalanobis distances in material clusters extracted from hyperspectral imagery data. An automated method for determining Johnson distribution parameters is used to model Mahalanobis distance distributions and is compared to an existing method which uses mixtures of F distributions. The results lead to a method for determining outliers and mitigating their effects.

关键词： Data modeling Hyperspectral imaging Mahalanobis distance expectation maximization algorithms Statistical analysis Mendelevium Scanning electron microscopy Solids Buildings Image segmentation

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