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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data IV"

共 516 条记录，以下是211-220 订阅

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UAV Panoramic images for Waterfront Landscape Analysis and TopographicDB Texturing

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13th International conference on Computational Science and Its Applications (ICCSA)

作者： Brumana, Raffaella Barazzetti, Luigi Oreni, Daniela Roncoroni, Fabio Politecn Milan I-20133 Milan Italy

ISBN: (纸本)9783642396496

The integration of landscape into planning policies requires an articulated territorial approach supported by multi-dimensional and multi-temporal Spatial data Infrastructure. It requests to improve SDI able to generate innovative typologies of representation with respect to 'different points of view'. In fact, many landscape representations rely nowadays on models integrated by aerial images with weak possibilities to reproduce views similar to the human eye, especially the ones at a human height. A methodological approach of rediscovering and correlating to new current maps the long tradition of historical human eye perspectives, water front view maps and itinerary maps, has been here experimented. The paper investigates the potential of panoramic view reconstruction and simulation from images acquired by RC/UAV and by multisensor ground platforms. The main aim is to generate multiple views models able to simulate real scenarios at the height of the observer, at ground level and lower altitudes. In particular the use of panoramic images for TopographicDB texturing is discussed showing their potential in improving value content decoding of built environment and 3D city models, supporting planners with more realistic instruments in the Environmental Impact Assessment (EIA). At the same time, such representations can improve knowledge transfer to citizens, toward an acknowledged tourism and volunteered image data acquisition through I-devices applications. On the other side, limits and critical elements that need to be overcome by the research of new algorithms and new acquisition instruments are shown.

关键词： landscape UAV image orientation panoramic view historical map SDI Geospatial Web Services city modelling waterfronts 3D texturing

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Automatic alignment of large 3D data sets for facade reconstruction from car-mounted LIDAR measurements

Automatic alignment of large 3D data sets for facade reconst...

引用

International conference on Cognitive Infocommunications (CogInfoCom)

作者： Dmitry Chetverikov Oszkár Józsa iván Eichhardt MTA SZTAKI and ELTE Budapest Hungary MTA SZTAKI Budapest Hungary

Automatic reconstruction of large-scale outdoor objects like house facades is an important component of mixed-reality systems that model and visualise real world at different level of detail. The authors are involved in a project that utilises a car-mounted LIDAR to acquire a sequence 3D point clouds representing facades in a street. No GPS or IMU is used. Hundreds of point clouds need to be automatically aligned to obtain a realistic surface model of facades. In this paper, we present and compare two solutions to this complex registration problem. Our methods are based on two different, widely used techniques for registering two partially overlapping point clouds in presence of outliers. The proposed algorithms are capable of automatically detecting occasional misalignments. We analyse the operation of the algorithms paying special attention to the robustness, speed and optimal parameter setting.

关键词： Laser radar Three-dimensional displays Robustness Surface reconstruction Buildings image reconstruction Measurement by laser beam

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FAST FAN/PARALLEL BEAM CS-BASED LOW-DOSE CT reconstruction

FAST FAN/PARALLEL BEAM CS-BASED LOW-DOSE CT RECONSTRUCTION

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IEEE International conference on Acoustics, Speech, and Signal Processing

作者： SayedMasoud Hashemi Soosan Beheshti Patrick R. Gill Narinder S. Paul Richard S. C. Cobbold Institute of Biomaterials & Biomedical Eng. University of Toronto Toronto Canada Electrical Engineering Department Ryerson University Toronto Canada Rambus Labs Rambus Inc. Sunnyvale California USA

ISBN: (纸本)9781479903573

Low dose X-ray Computed Tomography (CT) is clinically desired to reduce the risk of cancer caused by X-ray radiation. Compressed Sensing (CS), which allows images to be formed from incomplete data, enables large dose reduction to be achieved. Though this remains to be clinically unrealized due to excessive computation times. In this paper we demonstrate a fast, complete CS-based l_2-TV minimizing CT reconstruction method applicable to both parallel and fan beam geometries to recover high quality images from highly undersampled (thus low-dose) data. We apply the fast pseudo-polar Fourier algorithm and the Central Slice Theorem to reduce the computation time of CS recovery. On a typical desktop computer, we are able to reconstruct a 512×512 CT image in approximately 30 seconds: a clinically-significant speedup compared to the many hours required by previous CS methods.

关键词： Computed Tomography Central Slice Theorem Pseudo-Polar Fourier Transform Compressed Sensing X-Ray Computed Tomography Compressed sensing desktop computers computational time reconstructions X-rays image quality CONTAINMENT SPRAY carbon monosulfide conditioned stimulus Cesium images BEAM Beams and girders

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Learning Classifiers from Chains of Multiple Interlinked RDF data Stores

Learning Classifiers from Chains of Multiple Interlinked RDF...

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IEEE International conference on Services Economics (SE)

作者： Harris T. Lin Vasant Honavar Department of Computer Science Iowa State University Ames IA USA

The emergence of many interlinked, physically distributed, and autonomously maintained RDF stores offers unprecedented opportunities for predictive modeling and knowledge discovery from such data. However existing machine learning approaches are limited in their applicability because it is neither desirable nor feasible to gather all of the data in a centralized location for analysis due to access, memory, bandwidth, computational restrictions, and sometimes privacy and confidentiality constraints. Against this background, we consider the problem of learning predictive models from multiple interlinked RDF stores. Specifically we: (i) introduce statistical query based formulations of several representative algorithms for learning classifiers from RDF data, (ii) introduce a distributed learning framework to learn classifiers from multiple interlinked RDF stores that form a chain, (iii) identify three special cases of RDF data fragmentation and describe effective strategies for learning predictive models in each case, (iv) consider a novel application of a matrix reconstruction technique from the field of Computerized Tomography [1] to approximate the statistics needed by the learning algorithm from projections using count queries, thus dramatically reducing the amount of information transmitted from the remote data sources to the learner, and (v) report results of experiments with a real-world social network data set (***), which demonstrate the feasibility of the proposed approach.

关键词： Resource description framework Distributed databases Predictive models Vectors image reconstruction Computed tomography Subspace constraints

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Support estimation for phase retrieval image reconstruction from sparse-aperture interferometry data

Support estimation for phase retrieval image reconstruction ...

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conference on image reconstruction from incomplete data VII

作者： DeSantis, Zachary J. Fienup, James R. Univ Rochester Inst Opt Rochester NY 14627 USA

ISBN: (纸本)9780819492173

Imaging interferometry suffers from sparse Fourier measurements, and, at the visible wavelengths, a lack of phase information, creating a need for an image reconstruction algorithm. A support constraint is useful for optimization but is often not known a priori. The two-point rule for finding an object support from the autocorrelation is limited in usefulness by the sparsity and non-uniformity of the Fourier data and is insufficient for image reconstruction. Compactness, a common prior, does not require knowledge of the support. Compactness penalizes solutions that have bright pixels away from the center, favoring soft-edged objects with a bright center and darker extremities. With regards to imaging hard-edged objects such as satellites, a support constraint is desired but unknown and compactness may be unfavorable. Combining various techniques, a method of simultaneously estimating the object's support and the object's intensity distribution is presented. Though all the optimization parameters are in the image domain, we are effectively performing phase retrieval at the measurement locations and interpolation between the sparse data points.

关键词： image reconstruction imaging interferometry interpolation phase retrieval support constraint

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Imaging from scattered fields: limited data and degrees of freedom

Imaging from scattered fields: limited data and degrees of f...

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conference on image reconstruction from incomplete data VII

作者： Ritter, R. Shane Fiddy, Michael A. Perigon Int Inc 931 Ind Dr Matthews NC 28105 USA Univ N Carolina Ctr Optoelect & Opt Commun Charlotte NC 28223 USA

ISBN: (纸本)9780819492173

We describe how the number of degrees of freedom associated with a scattering experiment provides a guide to the minimum number of source and receiver locations required to image the scattering target. Since the number of degrees of freedom is approximately fixed, additional measurements do not necessarily improve the image fidelity in the absence of any prior knowledge. We illustrate these observations using a fast nonlinear inverse scattering method.

关键词： Imaging inverse scattering degrees of freedom

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MR images from fewer data

MR images from fewer data

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conference on image reconstruction from incomplete data VII

作者： Vafadar, Bahareh Bones, Philip J. Univ Canterbury Dept Elect & Comp Engn Computat Imaging Grp Christchurch 1 New Zealand

ISBN: (纸本)9780819492173

There is a strong motivation to reduce the amount of acquired data necessary to reconstruct clinically useful MR images, since less data means faster acquisition sequences, less time for the patient to remain motionless in the scanner and better time resolution for observing temporal changes within the body. We recently introduced an improvement in image quality for reconstructing parallel MR images by incorporating a data ordering step with compressed sensing (CS) in an algorithm named 'PECS'.(1) That method requires a prior estimate of the image to be available. We are extending the algorithm to explore ways of utilizing the data ordering step without requiring a prior estimate. The method presented here first reconstructs an initial image x(1) by compressed sensing (with scarcity enhanced by SVD), then derives a data ordering from x(1), R'(1), which ranks the voxels of x(1) according to their value. A second reconstruction is then performed which incorporates minimization of the first norm of the estimate after ordering by R'(1), resulting in a new reconstruction x(2). Preliminary results are encouraging.

关键词： Parallel Magnetic Resonance Imaging (pMRI) Compressed Sensing (CS)

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Superresolved image reconstruction from incomplete data

Superresolved Image Reconstruction from Incomplete Data

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conference on image reconstruction from incomplete data VII

作者： Chuang, Yi-Chen Dudley, Richard Fiddy, Michael A. Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Univ N Carolina Dept Phys & Opt Sci Charlotte NC 28223 USA Univ N Carolina Ctr Optoelect & Opt Commun Charlotte NC 28223 USA

ISBN: (纸本)9780819492173

A finite thickness slab of a metamaterial having a refractive index close to n = -1, can be used for sub-wavelength scale imaging. In the image domain, the measured fields contain evanescent wave contributions from subwavelength scale features in the object but these have to be related to the intrinsic parameters describing the scatterer such as refractive index or permittivity. For weak scatterers there can be a simple relationship between the field distribution and the permittivity profile. However for strong (multiple) scatterers and, more importantly, for objects for which subwavelength features contribute to the scattered (near) field, there is no simple relationship between the measured data and the permittivity profile. This is a significant inverse scattering problem for which no immediate solution exists and given the metamaterial slab's limitations one cannot assume that either angle or wavelength diversity will be available to apply an inverse scattering algorithm. We consider wavelength diversity in this paper to acquire the measured data necessary to estimate a superresolved solution to the inverse scattering problem.

关键词： Metamaterials superresolution scattering inverse scattering algorithm

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High-dynamic range interferometric astronomical imaging in the presence of direction dependent effects

High-dynamic range interferometric astronomical imaging in t...

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conference on image reconstruction from incomplete data VII

作者： Bhatnagar, S. Natl Radio Astron Observ Socorro NM 87801 USA

ISBN: (纸本)9780819492173

Modern high sensitivity radio interferometric telescopes use ultra wide-band receivers on a large number of antenna elements to achieve the capability of imaging dynamic ranges in excess of 1:1,000,000. In practice, the imaging performance is limited by instrumental and ionospheric/atmospheric effects that corrupt the recorded data. Many of these effects are directionally dependent and vary with time and frequency. Correcting for them is therefore fundamentally more difficult and these effects have been ignored in classical image reconstruction algorithms. Few attempts in the past to correct for these effects in the image-domain did not deliver the required accuracy. Recent developments in new algorithms that can account for such direction dependent effects show promising results. In this paper I give a general mathematical description of these techniques, show that the resulting algorithms are more optimal in terms of imaging performance and computing requirements and show some results.

关键词： Interferometric imaging image reconstruction Radio Astronomy Direction-dependent effects

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Single image Spatially Variant Out-of-focus Blur Removal

Single Image Spatially Variant Out-of-focus Blur Removal

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conference on image reconstruction from incomplete data VII

作者： Chan, Stanley H. Nguyen, Truong Q. Harvard Univ SEAS 33 Oxford St Cambridge MA 02138 USA UCSD Dept ECE La Jolla CA 92093 USA

ISBN: (纸本)9780819492173

This paper addresses the problem of two-layer out-of-focus blur removal from a single image, in which either the foreground or the background is in focus while the other is out of focus. To recover details from the blurry parts, the existing blind deconvolution algorithms are insufficient as the problem is spatially variant. The proposed method exploits the invariant structure of the problem by first predicting the occluded background. Then a blind deconvolution algorithm is applied to estimate the blur kernel and a coarse estimate of the image is found as a side product. Finally, the blurred region is recovered using total variation minimization, and fused with the sharp region to produce the final deblurred image.

关键词： Blind deconvolution spatially variant total variation alpha-matting kernel estimation depth

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