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

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Model-based reconstruction of organ surfaces from two-dimensional CT or MRT data of the head

Model-based reconstruction of organ surfaces from two-dimens...

引用

Medical Imaging 1999 conference - image Processing

作者： von Klinski, S Glausch, A Tolxdorff, T Free Univ Berlin Hosp Benjamin Franklin Dept Med Informat D-1000 Berlin Germany

ISBN: (纸本)0819431338

Surface-based interpolation and registration, radiation treatment, and three-dimensional visualization of two-dimensional sliced data from CT or MRT require a precise reconstruction of three-dimensional organ surfaces from two-dimensional segmentation results. Current surface-reconstruction algorithms are based on surface triangulations using heuristics to correlate and connect adjacent object slices. The approaches described in the literature can be divided into triangulations using optimization procedures, Delauny triangulations, and topology-based correlations. All approaches assume a global and invariant vertically oriented correlation strategy that can be applied equally to every organ and every slice. Surface and correlation characteristics vary greatly among bony structures and organs such as the eyes and the brain. An adjusted reconstruction of each organ according to its individual tissue characteristics is necessary to avoid errors in following processing steps such as interpolation, registration, and radiation treatment. To this end, we have designed a model-based surface-reconstruction algorithm that takes individual surface characteristics into account and allows the integration of anatomical knowledge. Three-dimensional surface models are generated from sliced data or any other source of anatomical knowledge. These models are later adjusted to the segmentations, compensating for artifacts and incomplete data.

关键词： reconstruction triangulation warping interpolation

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Is Compressive Sensing really Useful for Radar?

Is Compressive Sensing really Useful for Radar?

引用

IEEE Radar conference - from Sensing to Information

作者： Kemkemian, Stephane Nouvel-Fiani, Myriam Thales Airborne Syst Sensors Syst Tech Directorate Elancourt France

ISBN: (纸本)9781479920358

Compressive Sensing (CS) was originally developed to directly acquire a compressed description of a sparse scene. This can be possible provided that the sampling scheme meet certain criteria. In Radar applications, the main advantage highlighted in the literature is the reduction of the data-flow between the R.F. front-end and processing that would be intractable in number of cases. However thanks to the technological progresses in fast Analog to Digital Conversion (ADC) and subsequent digital processing, the real problem is not at this level except in some special cases. The real issue is that it is not always possible to sample the radar signal ideally for practical and uncontrollable reasons. So far, most of the work in the literature has focused on the mathematical aspect of the underlying L1 inverse problem. This paper shows some examples where the use of sparse or incomplete sampling is really justified. We also show that the inverse problem resolution based on the minimization of the L1 norm, even if it is elegant way to solve the problem, is not the only means to address the problem.

关键词： image reconstruction Noise Radar imaging Sensors Synthetic aperture radar vectors Inverse method Synthetic aperture radar Cloning vectors radar image reconstruction front-ends Sensors Noise Analog-digital conversion Radar applications Compressed sensing Radar imaging

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image reconstruction performance as a function of model complexity using information geometry: Application to transmission tomographic imaging

Image reconstruction performance as a function of model comp...

引用

conference on Computational Imaging v

作者： O'Sullivan, Joseph A. Xie, Liangjun Politte, David G. Whiting, Bruce R. Washington Univ Dept Elect & Syst Engn 1 Brookings DrCampus Box 1127 St Louis MO 63130 USA Washington Univ Sch Med Mallinckrodt Inst Radiol Elect Radiol Lab St Louis MO 63110 USA

ISBN: (纸本)9780819466112

Models used to derive image reconstruction algorithms typically make assumptions designed to increase the computational tractability of the algorithms while taking enough account of the physics to achieve desired performance. As the models for the physics become more detailed, the algorithms typically increase in complexity, often due to increases in the number of parameters in the models. When parameters are estimated from measured data and models of increased complexity include those of lower complexity as special cases, then as the number of parameters increases, model errors decrease and estimation errors increase. We adopt an information geometry approach to quantify the loss due to model errors and Fisher information to quantify the loss due to estimation errors. These are unified into one cost function. This approach is detailed in an X-ray transmission tomography problem where all models are approximations to the underlying problem defined on the continuum. Computations and simulations demonstrate the approach. The analysis provides tools for determining an appropriate model complexity for a given problem and bounds on information that can be extracted.

关键词： data modeling Error analysis Sensors reconstruction algorithms data analysis Algorithm development Statistical modeling Systems modeling image restoration Physics

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Application-Driven MRI: Joint reconstruction and Segmentation from Undersampled MRI data

Application-Driven MRI: Joint Reconstruction and Segmentatio...

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17th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Caballero, Jose Bai, Wenjia Price, Anthony N. Rueckert, Daniel Hajnal, Joseph v. Imperial Coll London Dept Comp London SW7 2AZ England St. Thomas Hosp Kings Coll London Div Imaging Sci Biomed Engn Dept London England

ISBN: (纸本)9783319104041;9783319104034

Medical image segmentation has traditionally been regarded as a separate process from image acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these first stages of the imaging pipeline. Adopting an integrated acquisition-reconstruction-segmentation process can provide a more efficient and accurate solution. In this paper we propose a joint segmentation and reconstruction algorithm for undersampled magnetic resonance data. Merging a reconstructive patch-based sparse modelling and a discriminative Gaussian mixture modelling can produce images with enhanced edge information ultimately improving their segmentation.

关键词： Magnetic resonance imaging

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Extraction of blood droplet flight trajectories from videos for forensic analysis

Extraction of blood droplet flight trajectories from videos ...

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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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Range image Super-resolution via reconstruction of Sparse Range data

Range Image Super-resolution via Reconstruction of Sparse Ra...

引用

International conference on Intelligent Systems and Signal Processing (ISSP)

作者： Bhavsar, Arnav v. Indian Inst Technol Madras Dept Elect Engg Madras Tamil Nadu India

ISBN: (纸本)9781479903177;9781479903160

We propose a method for super-resolution of range image. Our approach leverages the interpretation of LR image as sparse samples on the HR grid. Based on this interpretation, we build upon a recent approach which reconstructs dense range images from sparse range data. We notice certain shortcomings of this approach and propose some improvements, particularly, to address the super-resolution problem. Our method only uses a single colour image in addition to the range observation in the super-resolution process. Using the proposed approach, we demonstrate super-resolution results for large factors (e.g. 4 and 8) with good localization and accuracy.

关键词： Range images super-resolution segmentation

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3DMatch: Learning Local Geometric Descriptors from RGB-D reconstructions 30

3DMatch: Learning Local Geometric Descriptors from RGB-D Rec...

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30th IEEE/CvF conference on Computer vision and Pattern Recognition (CvPR)

作者： Zeng, Andy Song, Shuran Niessner, Matthias Fisher, Matthew Xiao, Jianxiong Funkhouser, Thomas Princeton Univ Princeton NJ 08544 USA Stanford Univ Stanford CA 94305 USA AutoX San Jose CA USA Adobe Syst San Jose CA USA

ISBN: (纸本)9781538604571

Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3D Match, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3D Match consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at http://***.

关键词： Three-dimensional displays image reconstruction Solid modeling Training Geometry Surface reconstruction

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Wave front distortion based fluid flow imaging

Wave front distortion based fluid flow imaging

引用

conference on image Processing - Machine vision Applications vI

作者： Iffa, Emishaw Heidrich, Wolfgang Univ British Columbia Dept Comp Sci Vancouver BC V6T 1W5 Canada

ISBN: (纸本)9780819494344

In this paper, a transparent flow surface reconstruction based on wave front distortion is investigated. A camera lens is used to focus the image formed by the micro-lens array to the camera imaging plane. The irradiance of the captured image is transformed to frequency spectrum and then the x and y spatial components are separated. A rigid spatial translation followed by low pass filtering yields a single frequency component of the image intensity. Index of refraction is estimated from the inverse Fourier transform of the spatial frequency spectrum of the irradiance. The proposed method is evaluated with synthetic data of a randomly generated index of refraction value and used to visualize a fuel injection volumetric data.

关键词： flow visualization Fourier demodulation index of refraction light field transparent flow wavefront reconstruction

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Effects of prior MRI information on image reconstruction in diffuse optical tomography.

Effects of prior MRI information on image reconstruction in ...

引用

conference on Novel Optical Instrumentation for Biomedical Applications

作者： Torregrossa, M Zint, Cv Poulet, P UMR 7004 CNRS Inst Phys Biol F-67085 Strasbourg France

ISBN: (纸本)0819450138

We describe a method to increase the speed of convergence for the simultaneous reconstruction of absorption and scattering images in Diffuse Optical Tomography (DOT). We used the diffusion approximation of the radiative transfer equation and the Finite Element Method (FEM) to solve the forward problem. The absorption and reduced scattering images are reconstructed by inverting the distribution of the moments of the time-dependent detected light flux. The inverse problem is solved with an optimization algorithm like ART or Conjugate Gradient. This ill-posed inverse problem can be simplified by using a priori knowledge of the studied objects. In this paper, we consider that DOT is a functional imaging technique that can be complemented by an anatomical imaging technique like Magnetic Resonance Imaging (MRI). We used anatomic information obtained from MRI as prior knowledge to compute optical absorption and scattering images. In a first step, MRI segmented images were only used to mesh our phantoms, with a finer resolution around boundaries. In a second step, we computed optical images with homogeneous properties from the segmented MRI image, in order to initialise our optimisation process. These two initialisations yield better reconstructed images. reconstruction from simulated and experimental data will be presented.

关键词： optical tomography image reconstruction a-priori knowledge

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A Robust Iterative Multiframe Super-Resolution reconstruction using a Huber Statistical Estimation Technique

A Robust Iterative Multiframe Super-Resolution Reconstructio...

引用

1st International conference on Communications and Networking in China

作者： Patanavijit, v Jitapunkul, S. Chulalongkorn Univ Fac Engn Elect Engn Dept Bangkok Thailand

ISBN: (纸本)1424404630

The traditional SRR (Super-Resolution reconstruction) estimations are based on L1 or L2 statistical norm estimation therefore these SRR methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate SRR approach based on a statistical estimation technique. By minimizing a cost function, the Huber norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data and Tikhonov regularization is used to remove artifacts from the final answer and improve the rate of convergence. The experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for a several noise models such as noiseless, AWGN, Poisson and Salt&Pepper Noise.

关键词： SRR (Super-Resolution reconstruction) Robust Estimation Huber Norm Regularized ML

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