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

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Extraction of blood droplet flight trajectories from videos for forensic analysis

Extraction of blood droplet flight trajectories from videos ...

引用

1st International conference on Pattern Recognition Applications and Methods, ICPRAM 2012

作者： Zarrabeitia, L.A. Aruliah, D.A. Qureshi, F.Z. Faculty of Science University of Ontario Institute of Technology Oshawa ON Canada

ISBN: (纸本)9789898425980

We present a method for extracting three-dimensional flight trajectories of liquid droplets from video data. A high-speed stereo camera pair records videos of experimental reconstructions of projectile impacts and ensuing droplet scattering. After background removal and segmentation of individual droplets in each video frame, we introduce a model-based matching technique to accumulate image paths for individual droplets. Our motion detection algorithm is designed to deal gracefully with the lack of feature points, with the similarity of droplets in shape, size, and color, and with incomplete droplet paths due to noise, occlusions, etc. The final reconstruction algorithm pairs two-dimensional paths accumulated from each of the two cameras'videos to reconstruct trajectories in three dimensions. The reconstructed droplet trajectories constitute a starting point for a physically accurate model of blood droplet flight for forensic bloodstain pattern analysis.

关键词： image reconstruction

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Limited Angle reconstruction Utilizing Improved Particle Swarm Optimization

Limited Angle Reconstruction Utilizing Improved Particle Swa...

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International conference on Biomedical Engineering and Biotechnology (iCBEB)

作者： Chenlin Du Junhai Wen Donghao Shi Rui Yang Department of Biomedical Engineering School of Life Science Beijing Institute of Technology Beijing China

Computed tomography technology has wide applications in industrial non-destructive tests, medical fields, and public security. In practice, incomplete projection is often acquired because of limitations on the detection environment, time and cost. Iterative reconstruction algorithm has a major potential to achieve superior results on the reconstruction with incomplete projection data. In this paper, we presented an iterative approach utilizing improved Particle Swarm Optimization, which can efficiently reconstruct the image from incomplete projection data by solving global optimization problem. Simulation results showed that the proposed algorithm has better convergence speed and reconstruction quality.

关键词： image reconstruction Particle swarm optimization Computed tomography Optimization Convergence Iterative methods Algorithm design and analysis

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DECA: Recovering fields of physical quantities from incomplete sensory data

DECA: Recovering fields of physical quantities from incomple...

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IEEE Communications Society conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Liu Xiang Jun Luo Chenwei Deng Athanasios V. Vasilakos Weisi Lin School of Computer Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering National and Technical University of Athens Greece

Although wireless sensor networks (WSNs) are powerful in monitoring physical events, the data collected from a WSN are almost always incomplete if the surveyed physical event spreads over a wide area. The reason for this incompleteness is twofold: i) insufficient network coverage and ii) data aggregation for energy saving. Whereas the existing recovery schemes only tackle the second aspect, we develop Dual-lEvel Compressed Aggregation (DECA) as a novel framework to address both aspects. Specifically, DECA allows a high fidelity recovery of a widespread event, under the situations that the WSN only sparsely covers the event area and that an in-network data aggregation is applied for traffic reduction. Exploiting both the low-rank nature of real-world events and the redundancy in sensory data, DECA combines matrix completion with a fine-tuned compressed sensing technique to conduct a dual-level reconstruction process. We demonstrate that DECA can recover a widespread event with less than 5% of the data (with respect to the dimension of the event) being collected. Performance evaluation based on both synthetic and real data sets confirms the recovery fidelity and energy efficiency of our DECA framework.

关键词： Wireless sensor networks Correlation Monitoring Routing Vectors image coding Eigenvalues and eigenfunctions

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image reconstruction by Prioritized Incremental Normalized Convolution 1

引用

17th Scandinavian conference on image Analysis (SCIA)

作者： Landstrom, Anders Nellros, Frida Jonsson, Hakan Thurley, Matthew Lulea Univ Technol Dept Comp Sci Elect & Space Engn S-97187 Lulea Sweden

ISBN: (数字)9783642212277

ISBN: (纸本)9783642212277

A priority-based method for pixel reconstruction and incremental hole filling in incomplete images and 3D surface data is presented. The method is primarily intended for reconstruction of occluded areas in 3D surfaces and makes use of a novel prioritizing scheme, based on a pixelwise defined confidence measure, that determines the order in which pixels are iteratively reconstructed. The actual reconstruction of individual pixels is performed by interpolation using normalized convolution. The presented approach has been applied to the problem of reconstructing 3D surface data of a rock pile as well as randomly sampled image data. It is concluded that the method is not optimal in the latter case, but the results show an improvement to ordinary normalized convolution when applied to the rock data and are in this case comparable to those obtained from normalized convolution using adaptive neighborhood sizes.

关键词： image reconstruction hole filling normalized convolution

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Sequential Regularized MLEM reconstruction Method for incomplete Sinograms

Sequential Regularized MLEM Reconstruction Method for Incomp...

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IEEE Nuclear Science Symposium/Medical Imaging conference (NSS/MIC)/18th International Workshop on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors

作者： Tuna, Uygar Pepe, Antonietta Ruotsalainen, Ulla Tampere Univ Technol Dept Signal Proc BioMediTech FIN-33101 Tampere Finland

ISBN: (纸本)9781467301206

In this study, we investigate the capability of the well-known maximum likelihood expectation maximization (MLEM) method in handling the incomplete sinogram reconstruction. MLEM method is known to be able to deal with the missing parts via system matrix modeling. We propose sequentially applied regularized MLEM method for the image reconstruction from the incomplete sinograms. Median filtering (MED), L-filtering (L) and block matching 3D (BM3D) filtering were employed as spatial domain regularizers, separately. The MLEM method was applied sequentially by decreasing gradually the amount of the regularization when the image update between the consecutive iterations was negligible. The sequential application of the MLEM method is used to compensate for the blurring caused by the regularization filters. In order to test the proposed approach thoroughly, we constructed wide test dataset consisting of three sparse sinograms (by using 25%, 10% and 5.5% of the sinogram data), the ECAT HRRT type sinogram (81.2% of the sinogram data), ClearPET type sinogram (88.3% of the sinogram data), AX-PET type sinogram (56% of the sinogram data) and a hypothetical checkerboard type sinogram (50% of the sinogram data) of the Shepp-Logan phantom. The constructed incomplete sinograms were contaminated with the Poisson noise. The incomplete sinograms were reconstructed by using filtered backprojection, MLEM without filtering, MLEM with MED, MLEM with L and MLEM with BM3D filtering. We evaluated the approach visually and with the quantitative normalized mean square error (NMSE). The results showed that MLEM without filtering cannot correct the artifacts related to the missing sinogram bins for some of the generated incomplete sinograms. On the other hand, MLEM method with spatial regularization filter was able to provide successfully reconstructed images for all cases. Quantitative NMSE values were in parallel with the visual impression of the reconstructed images.

关键词： Block matching and 3D filtering L filter median filtering missing sinogram bins quantitative evaluation region of interest regularized statistical reconstruction method sparse sinogram

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Quantitative and Qualitative Comparison of SAR images from incomplete Measurements Using Compressed Sensing and Nonuniform FFT

Quantitative and Qualitative Comparison of SAR Images from I...

引用

IEEE Radar conference (RADAR)

作者： Kajbaf, Hamed Case, Joseph T. Zheng, Yahong Rosa Kharkovsky, Sergey Zoughi, Reza Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

ISBN: (纸本)9781424489022

In this paper the performance of two wideband synthetic aperture radar (SAR) imaging methods from incomplete data sets are compared quantitatively and qualitatively. The first approach uses nonuniform fast Fourier transform (NUFFT) SAR to form images from nonuniform spatial and frequency data points. The second approach benefits from the emerging compressed sensing (CS) methodology to recover raw data from undersampled measurements. The results of our experimental tests show that CS has a better performance in terms of error and image contrast while NUFFT SAR has lower computational complexity.

关键词： Fast Fourier transforms image reconstruction Imaging Measurement Probes Signal to noise ratio Three dimensional displays compressed sensing fast Fourier transform fast Fourier transforms incomplete measurements nonuniform FFT radar imaging synthetic apert

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Evaluation of 2D image reconstruction using fan-beam FBP algorithm with no backprojection weight from WLP completed truncated projection data

Evaluation of 2D image reconstruction using fan-beam FBP alg...

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2011 4th International conference on Biomedical Engineering and Informatics, BMEI 2011

作者： Sumith, K. Narasimhadhan, A.V. Rajgopal, Kasi Dept. of Electrical Engg Indian Institute of Science Bangalore India

ISBN: (纸本)9781424493524

This paper presents the image reconstruction using the fan-beam filtered backprojection (FBP) algorithm with no backprojection weight from windowed linear prediction (WLP) completed truncated projection data. The image reconstruction from truncated projections aims to reconstruct the object accurately from the available limited projection data. Due to the incomplete projection data, the reconstructed image contains truncation artifacts which extends into the region of interest (ROI) making the reconstructed image unsuitable for further use. data completion techniques have been shown to be effective in such situations. We use windowed linear prediction technique for projection completion and then use the fan-beam FBP algorithm with no backprojection weight for the 2-D image reconstruction. We evaluate the quality of the reconstructed image using fan-beam FBP algorithm with no backprojection weight after WLP completion. © 2011 IEEE.

关键词： image reconstruction

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SENSITIVITY TO ERROR OF THE TRUNCATED HILBERT TRANSFORM TECHNIQUE FOR INTERIOR reconstruction

SENSITIVITY TO ERROR OF THE TRUNCATED HILBERT TRANSFORM TECH...

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18th IEEE International conference on image Processing (ICIP)

作者： Opie, A. M. T. Bones, P. J. Univ Canterbury Dept Elect & Comp Engn Christchurch 1 New Zealand

ISBN: (纸本)9781457713033

Historically, computed tomography reconstructions from truncated projection data have been considered non-unique. However, several recent results suggest that if the density of the object is known on some small region within the region of interest (ROI) then a unique and stable reconstruction of the complete ROI may be possible. Unfortunately, prior knowledge of the exact density of an object being scanned is uncommon, and as such an experimentally determined estimate must be used in its place. This estimate will naturally contain errors. We have performed several reconstruction simulations to establish the sensitivity of the proposed algorithm to errors in the prior knowledge. Our results suggest that the most important element of the prior knowledge is its mean value, while perturbations of the details cause fewer problems in the reconstructed images.

关键词： Computed tomography incomplete data sensitivity to error

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Limited data image reconstruction in optoacoustic tomography by constrained, total variation minimization

Limited data image reconstruction in optoacoustic tomography...

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conference on Photons Plus Ultrasound - Imaging and Sensing

作者： Wang, Kun Sidky, Emil Y. Anastasio, Mark A. Oraevsky, Alexander A. Pan, Xiaochuan Washington Univ Dept Biomed Engn St Louis MO 63130 USA Univ Chicago Dept Radiol MC 2026 Chicago IL 60637 USA TomoWave Labs Houston TX 77057 USA

ISBN: (纸本)9780819484369

Optoacoustic Tomography (OAT) is an emerging hybrid imaging technique with great potential for a wide range of biomedical imaging applications. Assuming point-like transducers, analytic algorithms are available for image reconstruction, but they are applicable only when the measured data are densely sampled on an aperture that encloses the object. In many cases of practical interest, however, measurements may be limited in number and are acquired on an incomplete aperture. Total variation (TV) minimization has been proved to be a powerful tool for limited-data reconstruction. However, most previous studies of limited-data OAT were based on an approximate imaging model that assumed point-like transducers, which limits the improvements on the reconstructed OAT image quality. In this work, we develop and investigate an iterative reconstruction algorithm incorporating ultrasonic transducer properties applicable for limited-data OAT. The algorithm is based on the minimization of the image TV subject to a data consistency condition, and is conceptually and mathematically distinct from classic iterative reconstruction algorithms. Preliminary computer-simulation studies are conducted to investigate the proposed algorithm. These studies reveal that the constrained, total variation minimization algorithm can yield accurate reconstructions in many limited-data applications where classic algorithms do not perform well.

关键词： optoacoustic tomography photoacoustic tomography iterative image reconstruction total variation minimization

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PET image reconstruction from Finite Linogram via Direct Fourier and Logarithmic Barrier Method

PET Image Reconstruction from Finite Linogram via Direct Fou...

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IEEE Nuclear Science Symposium/Medical Imaging conference (NSS/MIC)/18th International Workshop on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors

作者： Deng, Zhenzhou Xie, Qingguo Duan, Zhiwen Wuhan Natl Lab Optoelect Wuhan Hubei Peoples R China

ISBN: (纸本)9781467301206

Dual-head PET (Positron Emission Tomography) and PEM (Positron Emission Mammography) are encountered in many biological imaging and medical diagnosis applications. Since the data produced by flat-panel detectors are more closed to the linogram, and rebinning to the sinogram causes errors of the interpolation, image reconstruction based on the linogram is superior to keep the feature of the raw data for dual-head. Length of the linogram produced by actual PET or PEM systems is finite in practice. The projection views of the data are incomplete, and then the finite linogram reconstruction is a limited-view problem in mathematics. Conventional algorithms cannot achieve the exact reconstruction of the limited-view problem. In this work, we propose a half-analytic and half-iterative method named DF/LBM (Direct Fourier and Logarithmic Barrier Method) to solve the limited-view problem. The least square solution is obtained via DFM (Direct Fourier Method), and then a convex optimization method named logarithmic barrier method is employed to correct the least square solution. The substance of the convex optimization is defining the components in the null-space to satisfy some prior information, as the least square solution has no components in the null-space. Applying this method to Hoffman brain phantom, the artifact generated by the least square solution is eliminated, and PSNR is 90.2112, 94.8699 and 99.0507 when the tangent of the half allowable angle is 1.0, 1.5 and 2.0 respectively.

关键词： Barrier Contraception image reconstruction Logarithmic Finite Fourier sinogram Convex function optimization method

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