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检索条件"主题词=Expectation maximization algorithms"

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Unsupervised change detection methods for remote sensing images

Unsupervised change detection methods for remote sensing ima...

引用

Conference on Image and Signal Processing for Remote Sensing VII

作者： Melgani, F Moser, G Serpico, SB Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

ISBN: (纸本)0819442666

An unsupervised change detection problem can be viewed as a classification problem with only two classes corresponding to the change and no-change areas, respectively. Due to its simplicity, image differencing represents a popular approach for change detection. It is based on the idea to generate a difference image that represents the modulus of the spectral change vector associated to each pixel in the study area. To separate the change and no-change classes in the difference image, a simple thresholding-based procedure can be applied. However, the selection of the best threshold value is not a trivial problem. In the present work, several simple thresholding methods are investigated and compared. The combination of the expectation-maximization algorithm with a thresholding method is also considered with the aim of achieving a better estimation of the optimal threshold value. For experimental purpose, a study area affected by a forest fire is considered. Two Landsat TM images of the area acquired before and after the event are utilized to reveal the burned zones and to assess and compare the above mentioned unsupervised change detection methods.

关键词： expectation maximization algorithms Principal component analysis Image classification Remote sensing Multispectral imaging Statistical analysis Earth observing sensors Landsat Algorithm development Detection and tracking algorithms

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Pi of the Sky in LSC-Virgo's EM follow-up in O1 Science Run

Pi of the Sky in LSC-Virgo's EM follow-up in O1 Science Run

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Conference on Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments

作者： Zadrozny, Adam Sokolowski, Marcin Mankiewicz, Lech Zarnecki, Aleksander Filip Natl Ctr Nucl Res Otwock Poland Curtin Univ Int Ctr Radio Astron Res Perth WA Australia ARC Ctr Excellence All Sky Astrophys CAASTRO Sydney NSW Australia Polish Acad Sci Ctr Theoret Phys Warsaw Poland Univ Warsaw Fac Phys Warsaw Poland

ISBN: (数字)9781510613553

ISBN: (纸本)9781510613553;9781510613546

Presented in this contribution are results gathered by Pi of the Sky during LSC-Virgo O1 science run. Pi of the Sky took part in LSC-Virgo's Electromagnetic (EM) Follow-up project during first science run of Advanced LIGO detectors between September 2015 and January 2016. LSC-Virgo's EM Follow-up is aimed for searching electromagnetic counterparts to gravitational wave transient candidates. Observing an event both in EM and gravitational wave band might be a important step forward to multi-messenger astronomy. The aim of this paper is to show algorithms used by Pi of the Sky for analysing data taken during the first science run and to present the corresponding results. Concepts of algorithms for the next science run will also be discussed.

关键词： Sensors Astronomy LIGO Telescopes Observatories expectation maximization algorithms Data analysis Electromagnetism Physics Polishing Robotics

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QUADTREE-STRUCTURED MULTIPLE RESOLUTION SEGMENTATION OF SOME COMPUTED IMAGES 15

QUADTREE-STRUCTURED MULTIPLE RESOLUTION SEGMENTATION OF SOME...

引用

CONF ON APPLICATIONS OF DIGITAL IMAGE PROCESSING 15

作者： LEI, TH ZHAO, Z Univ. of Maryland/Baltimore (United States)

ISBN: (纸本)0819409448

Image statistics of some computed images and statistical history of the corresponding quadtree are analyzed. A new multiple resolution segmentation (MRS) approach using quadtree for these computed images is presented. The results obtained by using this approach demonstrate the correctness of the derived statistical properties and efficacy of this MRS scheme.

关键词： Image segmentation Magnetic resonance imaging X-ray computed tomography Image resolution X-ray imaging X-rays Digital image processing Statistical analysis expectation maximization algorithms Image analysis

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REGULARIZED MULTIFRAME RESTORATION OF HUBBLE SPACE AND GROUND-BASED TELESCOPE IMAGES WITH PARALLEL IMPLEMENTATION 15

REGULARIZED MULTIFRAME RESTORATION OF HUBBLE SPACE AND GROUN...

引用

CONF ON APPLICATIONS OF DIGITAL IMAGE PROCESSING 15

作者： KLAUS, R BURKHARDT, H Technische Univ. Hamburg-Harburg (Germany)

ISBN: (纸本)0819409448

The spherical aberration of the primary mirror of the Hubble Space Telescope (HST) has seriously reduced the sensitivity and degraded the resolution of images from the instruments. After the first period of observation, most of the astronomic researchers do not try to make photometric measurements on original or somewhat restored images, but they see the opportunity to take the better resolution of the HST to upgrade their ground-based images. From that point of view, the need of multiframe restoration methods appeared. This paper first of all shows the problem of multiframe image restoration methods by looking at several well known single-frame methods and their generalization. The problem of week radiation in astronomical images is modelled by poisson processes which is well known from positron emission tomography. The unconstrained maximum likelihood method for this class of problems leads to an equation that can be solved by an expectation maximization type iterative algorithm. A regularizing subband method is proposed that incorporates the multiframe problem in a single-frame scheme by a subband type solution. The filtering for the subbands incorporates the model inherent freedom. Taking the simplest case of ideal low- and high-pass filters, there is still the choise of the cut-off frequency. The low frequency band is taken from the ground-based telescope with its good photometric property, the high frequency band is taken from the HST with its good resolution property. A transputer based parallel implementation with an underlying message passing system will be shown as a time efficient solution of the algorithms.

关键词： Point spread functions Space telescopes Telescopes expectation maximization algorithms Filtering (signal processing) Image processing Image restoration Image resolution Digital image processing Hubble Space Telescope

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Autonomously detecting the defective pixels in an imaging sensor array using a robust statistical technique

Autonomously detecting the defective pixels in an imaging se...

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Conference on Image Quality and System Performance V

作者： Ghosh, Siddhartha Marshall, Ian Freitas, Alex Univ Kent Comp Lab Canterbury CT2 7NF Kent England Univ Lancaster Ctr Environm Informat Lancaster LA1 4YQ England

ISBN: (纸本)9780819469809

We propose a statistical technique for autonomously detecting defective pixels in a CCD sensor array. Our data-driven analysis technique can autonomously identify a wide range of faulty and 'suspect' pixels (hypo-sensitive or hyper-sensitive pixels), without the need for any defect model or prior knowledge of the nature of pixel faults. We apply our technique to the autonomous detection of the defective pixels in regular images captured with a camera, equipped with a CCD.

关键词： expectation maximization algorithms Cameras Defect detection CCD cameras Imaging arrays Sensors Data modeling Calibration Charge-coupled devices Detector arrays

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PET IMAGE RECONSTRUCTION USING SIMULATED ANNEALING

PET IMAGE RECONSTRUCTION USING SIMULATED ANNEALING

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Conference on Image Processing - Medical Imaging 1995

作者： SUNDERMANN, E LEMAHIEU, I STATE UNIV GHENT DEPT ELECTR & INFORMAT SYSTB-9000 GHENTBELGIUM

ISBN: (纸本)0819417823

In positron emission tomography (PET) images have to be reconstructed from noisy projection data. The noise on the PET data can be modeled by a Poisson distribution. The development of statistical (iterative) reconstruction techniques addresses the problem of noise. In this paper we present the results of introducing the simulated annealing technique as a statistical reconstruction algorithm for PET. We have successfully implemented a reconstruction algorithm based upon simulated annealing, with paying particular attention to the fine-tuning of various parameters (cooling schedule, granularity, stopping rule, ...). In addition, we have developed a cost function more appropriate to the noise statistics (e.g. Poisson) and the reconstruction method (e.g. ML). The comparison with other reconstruction methods using computer phantom studies proves the potential power of the simulated annealing technique for the reconstruction of PET-images.

关键词： Reconstruction algorithms algorithms Positron emission tomography expectation maximization algorithms Data modeling Image quality Image restoration Image processing Signal attenuation Electronics

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Multiple Instance Learning Framework For Landmine Detection Using Ground Penetrating Radar

Multiple Instance Learning Framework For Landmine Detection ...

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Conference on Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI

作者： Manandhar, A. Morton, K. D. Collins, L. M. Torrione, P. A. Duke Univ Dept Elect & Comp Engn Durham NC 27705 USA

ISBN: (纸本)9780819485915

Ground Penetrating Radar (GPR) data provides a powerful technique to identify subsurface buried threats. Although GPR data contains a three-dimensional representation of the subsurface, object truth (i.e. labels and positions of true threat objects in training lanes) is often provided in only two dimensions (GPS coordinates along the earth's surface). To mitigate uncertainty in an object's location in depth, many successful feature extraction/object recognition techniques in GPR extract feature vectors from several depth regions, and attempt to combine information across these feature vectors to make final decisions. However, many machine learning techniques are not well suited for learning under these conditions. Multiple Instance Learning (MIL) is a type of supervised learning method in which labels are available for sets of samples, but not for individual samples. The goal of learning in MIL is to classify new sets of samples as they become available. This set-based framework is useful in processing GPR responses since features are often extracted independently from multiple un-labeled depth bins, and thus a set of features is produced at each potential threat location. In this work, a comparison of several previous approaches to MlL applied to landmine detection in GPR data is presented. One recent algorithm, the p-Posterior Mixture Model approach (pPMM) is given special attention, and several slight modifications to the pPMM approach are presented and compared.

关键词： Land mines General packet radio service Feature extraction expectation maximization algorithms Molecules Ground penetrating radar Machine learning Sensors Data acquisition Target detection

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BAYESIAN IMAGE-RECONSTRUCTION WITH VARIABLE BALANCING PARAMETER 8

BAYESIAN IMAGE-RECONSTRUCTION WITH VARIABLE BALANCING PARAME...

引用

8th SPIE Conference on Instrumentation in Astronomy

作者： NUNEZ, J LLACER, J UNIV BARCELONA DEPT ASTRON & METEOROLE-08028 BARCELONASPAIN

ISBN: (纸本)081941493X

This paper describes the Bayesian image reconstruction algorithm with entropy prior with space variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of high and low signal/noise ratio, thus avoiding the large residuals present in algorithms that use a constant balancing parameter. The space variant hyperparameter determines the relative weight between the prior information and the likelihood defining the degree of smoothness of the solution. To compute the variable hyperparameter we used a segmentation technique based on artificial neural networks of the Self- Organizing Map type. Using this technique we segmented the image in 25 regions and computed a different value of the hyperparameter for each one. We applied the method to the Hubble Space Telescope Cameras and to ground based CCD data.

关键词： Image segmentation Reconstruction algorithms Image processing algorithms and systems Stars expectation maximization algorithms Image restoration Algorithm development Point spread functions Artificial neural networks Cameras

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Decision-theoretic image retrieval

Decision-theoretic image retrieval

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Internet Multimedia Management Systems III Conference

作者： Vasconcelos, N HP Cambridge Res Lab Cambridge England

ISBN: (纸本)0819446416

The design of an effective architecture for image retrieval requires careful consideration of the interplay between the three major components of a retrieval system: feature transformation, feature representation, and similarity function. We present a review of ongoing work on a decision theoretic formulation of the retrieval problem that enables the design of systems where all components are optimized with respect to the same end-to-end performance criteria: the minimization of the probability of retrieval error. In addition to some previously published results on the theoretical characterization of the impact of the feature transformation and representation in the probability of error, we present an efficient algorithm for optimal feature selection. Experimental results show that decision-theoretic retrieval performs well on color, texture, and generic image databases in terms of both retrieval accuracy and perceptual relevance of similarity judgments.

关键词： Databases Feature extraction Image retrieval Error analysis Independent component analysis expectation maximization algorithms Principal component analysis Feature selection Wavelets Visualization

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TRAINING A BOLTZMANN MACHINE FOR EDGE-PRESERVING IMAGE-RESTORATION 2

TRAINING A BOLTZMANN MACHINE FOR EDGE-PRESERVING IMAGE-RESTO...

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CONF ON NEURAL AND STOCHASTIC METHODS IN IMAGE AND SIGNAL PROCESSING 2

作者： BEDINI, L PANDOLFI, S TONAZZINI, A Istituto di Elaborazione della Informazione (Italy)

ISBN: (纸本)0819412813

The most successful methods to stabilize inverse ill-posed problems in visual reconstruction use a priori information on the local regularity of the image as well as constraints on the geometry of the discontinuities. A commonly used method to incorporate prior knowledge into the problem is to adopt a Bayesian approach in which the image is modelled by a parametric Gibbs prior and the solution is obtained by minimizing the resulting posterior energy function (MAP estimate). However, this approach presents two major difficulties: the first is related to the non-convexity of the function to be optimized; the second to the choice of the model parameters that best fit the available prior knowledge. Since these parameters strongly affect the quality of the reconstructions, their selection is a critical task. They are usually determined empirically by trial and error. The paper proposes a generalized Boltzmann Machine which makes it possible to learn the most appropriate parameters for a given class of images from a series of examples. The trained Boltzmann Machine is then used to implement an annealing scheme for the minimization of the non-convex posterior energy. The method is applied to the restoration of piecewise smooth images.

关键词： Neurons Chemical elements Image restoration Stochastic processes expectation maximization algorithms Signal processing Binary data Magnetorheological finishing Annealing Visualization

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